akameswa/mixtral-4x7b-instruct-code-old

mixtral-4x7b-instruct-code

mixtral-4x7b-instruct-code is a MoE of the following models using mergekit:

• akameswa/mistral-7b-instruct-javascript-16bit
• akameswa/mistral-7b-instruct-java-16bit
• akameswa/mistral-7b-instruct-cpp-16bit
• akameswa/mistral-7b-instruct-python-16bit

🧩 Configuration

base_model: akameswa/mistral-7b-instruct-v0.2-bnb-16bit
gate_mode: hidden 
dtype: float16 
experts:
  - source_model: akameswa/mistral-7b-instruct-javascript-16bit
    positive_prompts: ["You are helpful a coding assistant good at javascript"]
  - source_model: akameswa/mistral-7b-instruct-java-16bit
    positive_prompts: ["You are helpful a coding assistant good at java"]
  - source_model: akameswa/mistral-7b-instruct-cpp-16bit
    positive_prompts: ["You are helpful a coding assistant good at cpp"]
  - source_model: akameswa/mistral-7b-instruct-python-16bit
    positive_prompts: ["You are helpful a coding assistant good at python"]

Inference

from transformers import AutoTokenizer
import transformers
import torch

model = "akameswa/mixtral-4x7b-instruct-code-trial"
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",
    model_kwargs={"load_in_4bit": True},
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)

• Link to inference notebook