metadata

base_model:
  - huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated
library_name: transformers
tags:
  - Text Generation
  - text-generation-inference
  - Inference Endpoints
  - Transformers
  - Fusion
language:
  - en

DeepSeek-R1-Distill-Qwen-Coder-32B-Fusion-9010

Overview

DeepSeek-R1-Distill-Qwen-Coder-32B-Fusion-9010 is a mixed model that combines the strengths of two powerful DeepSeek-R1-Distill-Qwen-based models: huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated and huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated.

Although it's a simple mix, the model is usable, and no gibberish has appeared.

This is an experiment. Improve thinking abilities in programming and code. If any of the models meet your expectations, please give a thumbs up. This will help us finalize which model best meets everyone's expectations.

Model Details

Base Models:
- huihui-ai/DeepSeek-R1-Distill-Qwen-32B-abliterated (90%)
- huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated (10%)
Model Size: 32B parameters
Architecture: Qwen2.5
Mixing Ratio: 9:1 (DeepSeek-R1-Distill-Qwen-32B-abliterated:Qwen2.5-Coder-32B-Instruct-abliterated)

Usage

You can use this mixed model in your applications by loading it with Hugging Face's transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# Load the model and tokenizer
model_name = "huihui-ai/DeepSeek-R1-Distill-Qwen-Coder-32B-Fusion-9010"
#quant_config_4 = BitsAndBytesConfig(
#    load_in_4bit=True,
#    bnb_4bit_compute_dtype=torch.bfloat16,
#    bnb_4bit_use_double_quant=True,
#    llm_int8_enable_fp32_cpu_offload=True,
#)

quant_config_8 = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_enable_fp32_cpu_offload=True,
    llm_int8_has_fp16_weight=True,
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    quantization_config=quant_config_8,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Initialize conversation context
initial_messages = [
    {"role": "system", "content": "You are a helpful assistant."}
]
messages = initial_messages.copy()  # Copy the initial conversation context

# Enter conversation loop
while True:
    # Get user input
    user_input = input("User: ").strip()  # Strip leading and trailing spaces

    # If the user types '/exit', end the conversation
    if user_input.lower() == "/exit":
        print("Exiting chat.")
        break

    # If the user types '/clean', reset the conversation context
    if user_input.lower() == "/clean":
        messages = initial_messages.copy()  # Reset conversation context
        print("Chat history cleared. Starting a new conversation.")
        continue

    # If input is empty, prompt the user and continue
    if not user_input:
        print("Input cannot be empty. Please enter something.")
        continue

    # Add user input to the conversation
    messages.append({"role": "user", "content": user_input})

    # Build the chat template
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    # Tokenize input and prepare it for the model
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    # Generate a response from the model
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=8192
    )

    # Extract model output, removing special tokens
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

    # Add the model's response to the conversation
    messages.append({"role": "assistant", "content": response})

    # Print the model's response
    print(f"Response: {response}")

Use with ollama

You can use huihui_ai/deepseek-r1-Fusion directly

ollama run huihui_ai/deepseek-r1-Fusion

Donation

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