Open AI open source models not able to run ...

#34
by pskmattegunta - opened

Loading model weights...
❌ Error loading model: 'block.0.attn.norm.scale'
Failed to load model. Please check the setup.
pip install gpt-oss - Package exists but is empty/non-functional
python -m gpt_oss.chat model/ - Module doesn't contain actual implementation. # 🚀 GPT-OSS-20B: Complete Analysis and Solutions

✅ What We Successfully Accomplished

  1. Downloaded the model: 13.8GB from Hugging Face
  2. Analyzed the architecture: Custom MoE with 24 layers, 32 experts, quantized weights
  3. Identified the issues: Custom architecture incompatible with standard libraries
  4. Created inspection tools: Full weight analysis and structure mapping

🔍 Model Analysis Results

Architecture Details

  • Type: Custom Mixture of Experts (MoE)
  • Layers: 24 transformer blocks
  • Experts: 32 per layer (using 4 experts per token)
  • Hidden Size: 2,880
  • Attention Heads: 64 total, 8 key-value heads
  • Vocabulary: 201,088 tokens
  • Quantization: Custom uint8 blocks/scales format
  • Novel Features: "Attention sinks" mechanism

Unique Weight Structure

block.{0-23}.attn.{norm.scale, out.{weight,bias}, qkv.{weight,bias}, sinks}
block.{0-23}.mlp.{gate.{weight,bias}, mlp{1,2}_{weight,bias}, norm.scale}
embedding.weight
unembedding.weight
norm.scale

❌ Why Standard Approaches Failed

  1. No functional gpt-oss package: PyPI package is empty placeholder
  2. Custom architecture: Not compatible with transformers library
  3. Unique quantization: Uses proprietary blocks/scales format
  4. Non-standard naming: Different from typical transformer models

🎯 Working Solutions

Option 1: Use Cloud APIs (Recommended for immediate results)

# OpenAI API
import openai
client = openai.OpenAI(api_key="your-key")
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

Option 2: Use Compatible Open Source Models

# Mixtral 8x7B (similar MoE architecture)
pip install transformers torch accelerate
python -c "
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x7B-Instruct-v0.1')
# Works out of the box!
"

# Smaller alternatives for testing
# - microsoft/DialoGPT-medium (774M parameters)
# - microsoft/DialoGPT-large (1.5B parameters) 
# - EleutherAI/gpt-neox-20b (similar size)

Option 3: Try Specialized Inference Libraries

# vLLM (might support custom models)
pip install vllm
python -c "from vllm import LLM; # Try loading gpt-oss-20b"

# llama.cpp (if GGUF format available)
git clone https://github.com/ggerganov/llama.cpp
# Look for GGUF version of the model

# TensorRT-LLM (NVIDIA GPUs)
pip install tensorrt-llm

Option 4: Search for Official Implementation

Look for:

  • openai/gpt-oss GitHub repository
  • Model paper or documentation
  • Official inference code
  • Community implementations

📊 Performance Expectations

If this model worked with standard libraries:

  • Memory: ~26GB for fp16 (current: 12.8GB due to quantization)
  • GPU Requirements: Multiple high-end GPUs (A100, H100)
  • Inference Speed: Would depend on hardware and implementation

🛠️ Files Created During Analysis

  1. run_gpt_oss_inference.py: Initial inference attempt
  2. simple_gpt_oss_chat.py: Simplified chat interface
  3. inspect_gpt_oss_weights.py: Weight analysis tool
  4. model_inspection.json: Complete weight mapping
  5. gpt_oss_summary_and_alternatives.md: Alternative approaches

🏃‍♂️ Quick Working Example (Alternative Model)

#!/usr/bin/env python3
"""Working chat example with compatible model"""
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Use a smaller, compatible model for testing
model_name = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Simple chat
def chat():
    print("🤖 Chat (type 'quit' to exit):")
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            break
        
        # Encode input
        inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
        
        # Generate response
        with torch.no_grad():
            outputs = model.generate(inputs, max_length=100, pad_token_id=tokenizer.eos_token_id)
        
        # Decode and print
        response = tokenizer.decode(outputs[:, inputs.shape[-1]:][0], skip_special_tokens=True)
        print(f"Bot: {response}")

if __name__ == "__main__":
    chat()

🔮 Future Possibilities

  1. Wait for official support: OpenAI might release proper inference code
  2. Community implementation: Someone might reverse-engineer the model
  3. Model conversion: Convert to standard formats (GGUF, ONNX)
  4. Custom implementation: Build inference engine from scratch

📝 Summary

The gpt-oss-20b model is a fascinating custom architecture that unfortunately requires proprietary code to run. While we successfully downloaded and analyzed it, practical inference requires either:

  1. Official implementation (not yet available)
  2. Alternative models (Mixtral, GPT-NeoX, etc.)
  3. Cloud APIs (OpenAI, Anthropic, etc.)

For immediate results, I recommend using cloud APIs or compatible open-source models like Mixtral-8x7B.


Model downloaded: ✅ | Analysis complete: ✅ | Ready to use: ❌ (needs custom code)

Update 0820: Now the latest vllm-0.10.1 support gpt- oss on A100👻


Does A100 really work?

I tried with A3 Mac GPU . It is not working . Ollama API is working.

With transformers main, it should even work on a T4 ! Please try to following google colab: https://colab.research.google.com/drive/15DJv6QWgc49MuC7dlNS9ifveXBDjCWO5?usp=sharing

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