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RichardErkhov/
saucam_-_Phind-Codefuse-34B-gguf

Name: saucam_-_Phind-Codefuse-34B-gguf
Availability: InStock

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gguf

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Phind-Codefuse-34B - GGUF

Model creator: https://huggingface.co/saucam/
Original model: https://huggingface.co/saucam/Phind-Codefuse-34B/

Name	Quant method	Size
Phind-Codefuse-34B.Q2_K.gguf	Q2_K	11.65GB
Phind-Codefuse-34B.IQ3_XS.gguf	IQ3_XS	12.93GB
Phind-Codefuse-34B.IQ3_S.gguf	IQ3_S	13.65GB
Phind-Codefuse-34B.Q3_K_S.gguf	Q3_K_S	13.6GB
Phind-Codefuse-34B.IQ3_M.gguf	IQ3_M	14.18GB
Phind-Codefuse-34B.Q3_K.gguf	Q3_K	15.19GB
Phind-Codefuse-34B.Q3_K_M.gguf	Q3_K_M	15.19GB
Phind-Codefuse-34B.Q3_K_L.gguf	Q3_K_L	16.55GB
Phind-Codefuse-34B.IQ4_XS.gguf	IQ4_XS	16.99GB
Phind-Codefuse-34B.Q4_0.gguf	Q4_0	17.74GB
Phind-Codefuse-34B.IQ4_NL.gguf	IQ4_NL	17.92GB
Phind-Codefuse-34B.Q4_K_S.gguf	Q4_K_S	17.87GB
Phind-Codefuse-34B.Q4_K.gguf	Q4_K	18.83GB
Phind-Codefuse-34B.Q4_K_M.gguf	Q4_K_M	18.83GB
Phind-Codefuse-34B.Q4_1.gguf	Q4_1	19.69GB
Phind-Codefuse-34B.Q5_0.gguf	Q5_0	21.64GB
Phind-Codefuse-34B.Q5_K_S.gguf	Q5_K_S	21.64GB
Phind-Codefuse-34B.Q5_K.gguf	Q5_K	22.2GB
Phind-Codefuse-34B.Q5_K_M.gguf	Q5_K_M	22.2GB
Phind-Codefuse-34B.Q5_1.gguf	Q5_1	23.59GB
Phind-Codefuse-34B.Q6_K.gguf	Q6_K	25.78GB
Phind-Codefuse-34B.Q8_0.gguf	Q8_0	33.39GB

Original model description:

tags:

merge
mergekit
lazymergekit
Phind/Phind-CodeLlama-34B-v2
codefuse-ai/CodeFuse-CodeLlama-34B base_model:
Phind/Phind-CodeLlama-34B-v2
codefuse-ai/CodeFuse-CodeLlama-34B license: mit

Phind-Codefuse-34B

Phind-Codefuse-34B is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml

models:
  - model: Phind/Phind-CodeLlama-34B-v2
    parameters:
      density: 0.5
      weight: 0.6
    # No parameters necessary for base model
  - model: codefuse-ai/CodeFuse-CodeLlama-34B
    parameters:
      density: 0.5
      weight: 0.4
merge_method: task_arithmetic
base_model: Phind/Phind-CodeLlama-34B-v2
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "saucam/Phind-Codefuse-34B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

RichardErkhov/saucam_-_Phind-Codefuse-34B-gguf

Original model description:

Phind-Codefuse-34B

🧩 Configuration

💻 Usage

RichardErkhov/
saucam_-_Phind-Codefuse-34B-gguf