mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
gpt2 : Add gpt2 architecture integration (#4555)
This commit is contained in:
manikbhandari
GitHub
parent
f6793491b5
commit
ea5497df5d
@ -103,6 +103,7 @@ as the main playground for developing new features for the [ggml](https://github
|
||||
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
|
||||
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
|
||||
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
|
||||
- [x] [GPT-2](https://huggingface.co/gpt2)
|
||||
|
||||
**Multimodal models:**
|
||||
|
||||
|
||||
@ -182,6 +182,8 @@ class Model:
|
||||
return QwenModel
|
||||
if model_architecture == "MixtralForCausalLM":
|
||||
return MixtralModel
|
||||
if model_architecture == "GPT2LMHeadModel":
|
||||
return GPT2Model
|
||||
if model_architecture == "PhiForCausalLM":
|
||||
return Phi2Model
|
||||
if model_architecture == "PlamoForCausalLM":
|
||||
@ -225,6 +227,8 @@ class Model:
|
||||
return gguf.MODEL_ARCH.QWEN
|
||||
if arch == "MixtralForCausalLM":
|
||||
return gguf.MODEL_ARCH.LLAMA
|
||||
if arch == "GPT2LMHeadModel":
|
||||
return gguf.MODEL_ARCH.GPT2
|
||||
if arch == "PhiForCausalLM":
|
||||
return gguf.MODEL_ARCH.PHI2
|
||||
if arch == "PlamoForCausalLM":
|
||||
@ -993,6 +997,68 @@ class QwenModel(Model):
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
class GPT2Model(Model):
|
||||
def set_gguf_parameters(self):
|
||||
self.gguf_writer.add_name(self.dir_model.name)
|
||||
self.gguf_writer.add_block_count(self.hparams["n_layer"])
|
||||
self.gguf_writer.add_context_length(self.hparams["n_ctx"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
|
||||
self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
|
||||
self.gguf_writer.add_head_count(self.hparams["n_head"])
|
||||
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
|
||||
for name, data_torch in self.get_tensors():
|
||||
# we don't need these
|
||||
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")):
|
||||
continue
|
||||
|
||||
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")):
|
||||
data_torch = data_torch.transpose(1, 0)
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.ftype == 0 and data_dtype == np.float16:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
||||
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
# note: GPT2 output is tied to (same as) wte in original model
|
||||
if new_name == "token_embd.weight":
|
||||
print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
self.gguf_writer.add_tensor("output.weight", data)
|
||||
|
||||
|
||||
class Phi2Model(Model):
|
||||
def set_gguf_parameters(self):
|
||||
block_count = self.hparams["n_layer"]
|
||||
|
||||
@ -370,7 +370,16 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.GPT2: [
|
||||
# TODO
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.POS_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.PHI2: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
|
||||
@ -17,6 +17,7 @@ class TensorNameMap:
|
||||
"tok_embeddings", # llama-pth
|
||||
"embeddings.word_embeddings", # bert
|
||||
"language_model.embedding.word_embeddings", # persimmon
|
||||
"wte", # gpt2
|
||||
"transformer.embd.wte", # phi2
|
||||
),
|
||||
|
||||
@ -34,6 +35,7 @@ class TensorNameMap:
|
||||
MODEL_TENSOR.POS_EMBD: (
|
||||
"transformer.wpe", # gpt2
|
||||
"embeddings.position_embeddings", # bert
|
||||
"wpe", # gpt2
|
||||
),
|
||||
|
||||
# Output
|
||||
@ -53,7 +55,7 @@ class TensorNameMap:
|
||||
"norm", # llama-pth
|
||||
"embeddings.LayerNorm", # bert
|
||||
"transformer.norm_f", # mpt
|
||||
"ln_f", # refact bloom qwen
|
||||
"ln_f", # refact bloom qwen gpt2
|
||||
"language_model.encoder.final_layernorm", # persimmon
|
||||
"lm_head.ln", # phi2
|
||||
),
|
||||
@ -78,6 +80,7 @@ class TensorNameMap:
|
||||
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
|
||||
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
|
||||
"model.layers.{bid}.ln1", # yi
|
||||
"h.{bid}.ln_1", # gpt2
|
||||
"transformer.h.{bid}.ln", # phi2
|
||||
"model.layers.layers.{bid}.norm", # plamo
|
||||
),
|
||||
@ -95,6 +98,7 @@ class TensorNameMap:
|
||||
"transformer.h.{bid}.self_attention.query_key_value", # falcon
|
||||
"h.{bid}.self_attention.query_key_value", # bloom
|
||||
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
|
||||
"h.{bid}.attn.c_attn", # gpt2
|
||||
"transformer.h.{bid}.mixer.Wqkv", # phi2
|
||||
),
|
||||
|
||||
@ -137,6 +141,7 @@ class TensorNameMap:
|
||||
"encoder.layer.{bid}.attention.output.dense", # bert
|
||||
"transformer.h.{bid}.attn.out_proj", # gpt-j
|
||||
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
|
||||
"h.{bid}.attn.c_proj", # gpt2
|
||||
"transformer.h.{bid}.mixer.out_proj", # phi2
|
||||
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
|
||||
),
|
||||
@ -159,6 +164,7 @@ class TensorNameMap:
|
||||
"encoder.layer.{bid}.output.LayerNorm", # bert
|
||||
"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
|
||||
"model.layers.{bid}.ln2", # yi
|
||||
"h.{bid}.ln_2", # gpt2
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_GATE_INP: (
|
||||
@ -179,6 +185,7 @@ class TensorNameMap:
|
||||
"transformer.h.{bid}.mlp.fc_in", # gpt-j
|
||||
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
|
||||
"transformer.h.{bid}.mlp.w1", # qwen
|
||||
"h.{bid}.mlp.c_fc", # gpt2
|
||||
"transformer.h.{bid}.mlp.fc1", # phi2
|
||||
"model.layers.layers.{bid}.mlp.up_proj", # plamo
|
||||
),
|
||||
@ -218,6 +225,7 @@ class TensorNameMap:
|
||||
"encoder.layer.{bid}.output.dense", # bert
|
||||
"transformer.h.{bid}.mlp.fc_out", # gpt-j
|
||||
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
|
||||
"h.{bid}.mlp.c_proj", # gpt2
|
||||
"transformer.h.{bid}.mlp.fc2", # phi2
|
||||
"model.layers.layers.{bid}.mlp.down_proj", # plamo
|
||||
),
|
||||
|
||||
182
llama.cpp
182
llama.cpp
@ -423,6 +423,15 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
|
||||
LLM_ARCH_GPT2,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
||||
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
},
|
||||
},
|
||||
{
|
||||
@ -1256,6 +1265,10 @@ enum e_model {
|
||||
MODEL_40B,
|
||||
MODEL_65B,
|
||||
MODEL_70B,
|
||||
MODEL_SMALL,
|
||||
MODEL_MEDIUM,
|
||||
MODEL_LARGE,
|
||||
MODEL_XL,
|
||||
};
|
||||
|
||||
static const size_t kiB = 1024;
|
||||
@ -2563,6 +2576,10 @@ static const char * llama_model_type_name(e_model type) {
|
||||
case MODEL_40B: return "40B";
|
||||
case MODEL_65B: return "65B";
|
||||
case MODEL_70B: return "70B";
|
||||
case MODEL_SMALL: return "0.1B";
|
||||
case MODEL_MEDIUM: return "0.4B";
|
||||
case MODEL_LARGE: return "0.8B";
|
||||
case MODEL_XL: return "1.5B";
|
||||
default: return "?B";
|
||||
}
|
||||
}
|
||||
@ -2782,6 +2799,17 @@ static void llm_load_hparams(
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GPT2:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
||||
switch (hparams.n_layer) {
|
||||
case 12: model.type = e_model::MODEL_SMALL; break;
|
||||
case 24: model.type = e_model::MODEL_MEDIUM; break;
|
||||
case 36: model.type = e_model::MODEL_LARGE; break;
|
||||
case 48: model.type = e_model::MODEL_XL; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
|
||||
default: (void)0;
|
||||
}
|
||||
@ -3710,6 +3738,60 @@ static bool llm_load_tensors(
|
||||
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_GPT2:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
||||
model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
|
||||
|
||||
// output
|
||||
{
|
||||
ggml_backend_type backend_norm;
|
||||
ggml_backend_type backend_output;
|
||||
|
||||
if (n_gpu_layers > int(n_layer)) {
|
||||
backend_norm = llama_backend_offload;
|
||||
backend_output = llama_backend_offload_split;
|
||||
} else {
|
||||
backend_norm = GGML_BACKEND_CPU;
|
||||
backend_output = GGML_BACKEND_CPU;
|
||||
}
|
||||
|
||||
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
||||
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
|
||||
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
||||
}
|
||||
|
||||
const uint32_t n_ff = hparams.n_ff;
|
||||
|
||||
const int i_gpu_start = n_layer - n_gpu_layers;
|
||||
|
||||
model.layers.resize(n_layer);
|
||||
|
||||
for (uint32_t i = 0; i < n_layer; ++i) {
|
||||
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
|
||||
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
||||
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
|
||||
|
||||
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
|
||||
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend);
|
||||
|
||||
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
||||
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend);
|
||||
|
||||
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
||||
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
|
||||
|
||||
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
|
||||
layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend);
|
||||
|
||||
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
||||
layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend);
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
}
|
||||
@ -5754,6 +5836,102 @@ struct llm_build_context {
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_gpt2() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * pos;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
|
||||
cb(inpL, "inp_embd", -1);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
cb(inp_pos, "inp_pos", -1);
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
||||
cb(KQ_mask, "KQ_mask", -1);
|
||||
|
||||
pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
|
||||
cb(pos, "pos_embd", -1);
|
||||
|
||||
inpL = ggml_add(ctx0, inpL, pos);
|
||||
cb(inpL, "inpL", -1);
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm,
|
||||
model.layers[il].attn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
||||
cb(cur, "bqkv", il);
|
||||
|
||||
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
|
||||
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
|
||||
|
||||
cur = llm_build_kqv(ctx0, model, hparams, kv_self,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Qcur, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
cb(cur, "kqv_out", il);
|
||||
}
|
||||
|
||||
// add the input
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// FF
|
||||
{
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm,
|
||||
model.layers[il].ffn_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
||||
NULL, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
||||
NULL,
|
||||
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
inpL = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(inpL, "l_out", il);
|
||||
}
|
||||
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.output_norm,
|
||||
model.output_norm_b,
|
||||
LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
@ -6269,6 +6447,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_plamo();
|
||||
} break;
|
||||
case LLM_ARCH_GPT2:
|
||||
{
|
||||
result = llm.build_gpt2();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
BIN
models/ggml-vocab-gpt2.gguf
Normal file
BIN
models/ggml-vocab-gpt2.gguf
Normal file
Binary file not shown.
@ -41,6 +41,7 @@ llama_test_executable (test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cp
|
||||
llama_test_executable (test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
|
||||
llama_test_executable (test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
|
||||
llama_test_executable (test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
|
||||
llama_test_executable (test-tokenizer-1-gpt2 test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
|
||||
# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
|
||||
|
||||
llama_build_and_test_executable(test-grammar-parser.cpp)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user