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llama.cpp
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model: codefuse-ai/F2LLM-v2 support

This commit is contained in:
Saba Fallah 2026-03-21 10:27:44 +01:00
parent e06c3ab2bc
commit fafb17d321
10 changed files with 165 additions and 1 deletions
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8
convert_hf_to_gguf.py
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@ -1494,6 +1494,9 @@ class TextModel(ModelBase):
if chkhsh == "e4d54df1ebc1f2b91acd986c5b51aa50837d5faf7c7398e73c1f9e9ee5d19869":
# ref: https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601
res = "kanana2"
if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
# ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
res = "f2llmv2"
if res is None:
logger.warning("\n")
@ -4889,6 +4892,11 @@ class Glm4VVisionModel(Qwen3VLVisionModel):
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Qwen3Model")
class F2LLMv2Model(Qwen3Model):
model_arch = gguf.MODEL_ARCH.F2LLMV2
@ModelBase.register("Qwen3VLForConditionalGeneration")
class Qwen3VLTextModel(Qwen3Model):
model_arch = gguf.MODEL_ARCH.QWEN3VL

1
convert_hf_to_gguf_update.py
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@ -154,6 +154,7 @@ models = [
{"name": "qwen35", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3.5-9B-Instruct", },
{"name": "joyai-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jdopensource/JoyAI-LLM-Flash", },
{"name": "kanana2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601", },
{"name": "f2llmv2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/codefuse-ai/F2LLM-v2-4B", },
]
# some models are known to be broken upstream, so we will skip them as exceptions

19
gguf-py/gguf/constants.py
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@ -394,6 +394,7 @@ class MODEL_ARCH(IntEnum):
QWEN3MOE = auto()
QWEN3NEXT = auto()
QWEN3VL = auto()
F2LLMV2 = auto()
QWEN3VLMOE = auto()
QWEN35 = auto()
QWEN35MOE = auto()
@ -932,6 +933,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA_EMBED: "llama-embed",
MODEL_ARCH.MAINCODER: "maincoder",
MODEL_ARCH.KIMI_LINEAR: "kimi-linear",
MODEL_ARCH.F2LLMV2: "f2llmv2",
}
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@ -3694,6 +3696,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
MODEL_ARCH.F2LLMV2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
# TODO
}

1
src/CMakeLists.txt
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@ -66,6 +66,7 @@ add_library(llama
models/exaone-moe.cpp
models/exaone.cpp
models/exaone4.cpp
models/f2llmv2.cpp
models/falcon-h1.cpp
models/falcon.cpp
models/gemma-embedding.cpp

2
src/llama-arch.cpp
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@ -130,6 +130,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA_EMBED, "llama-embed" },
{ LLM_ARCH_MAINCODER, "maincoder" },
{ LLM_ARCH_KIMI_LINEAR, "kimi-linear" },
{ LLM_ARCH_F2LLMV2, "f2llmv2" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -955,6 +956,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_UP_SHEXP,
};
case LLM_ARCH_QWEN3:
case LLM_ARCH_F2LLMV2:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,

1
src/llama-arch.h
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@ -134,6 +134,7 @@ enum llm_arch {
LLM_ARCH_LLAMA_EMBED,
LLM_ARCH_MAINCODER,
LLM_ARCH_KIMI_LINEAR,
LLM_ARCH_F2LLMV2,
LLM_ARCH_UNKNOWN,
};

7
src/llama-model.cpp
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@ -994,6 +994,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
} break;
case LLM_ARCH_QWEN3:
case LLM_ARCH_F2LLMV2:
{
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -3632,6 +3633,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
} break;
case LLM_ARCH_QWEN3:
case LLM_ARCH_QWEN3VL:
case LLM_ARCH_F2LLMV2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@ -8666,6 +8668,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_step35_iswa>(*this, params);
} break;
case LLM_ARCH_F2LLMV2:
{
llm = std::make_unique<llm_build_f2llmv2>(*this, params);
} break;
default:
GGML_ABORT("fatal error");
}
@ -8916,6 +8922,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
case LLM_ARCH_F2LLMV2:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:

3
src/llama-vocab.cpp
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@ -1952,7 +1952,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
} else if (
tokenizer_pre == "qwen2" ||
tokenizer_pre == "deepseek-r1-qwen" ||
tokenizer_pre == "kormo") {
tokenizer_pre == "kormo" ||
tokenizer_pre == "f2llmv2") {
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
clean_spaces = false;
} else if (

120
src/models/f2llmv2.cpp Normal file
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@ -0,0 +1,120 @@
#include "models.h"
llm_build_f2llmv2::llm_build_f2llmv2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
GGML_ASSERT(n_embd_head == n_rot);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
// norm
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn,
model.layers[il].wo, model.layers[il].bo,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
if (model.layers[il].wo_s) {
cur = ggml_mul(ctx0, cur, model.layers[il].wo_s);
}
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}

4
src/models/models.h
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@ -227,6 +227,10 @@ struct llm_build_exaone_moe : public llm_graph_context {
llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_f2llmv2 : public llm_graph_context {
llm_build_f2llmv2(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_falcon : public llm_graph_context {
llm_build_falcon(const llama_model & model, const llm_graph_params & params);
};