Open AI open source models not able to run ...
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
- Downloaded the model: 13.8GB from Hugging Face
- Analyzed the architecture: Custom MoE with 24 layers, 32 experts, quantized weights
- Identified the issues: Custom architecture incompatible with standard libraries
- 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
- No functional gpt-oss package: PyPI package is empty placeholder
- Custom architecture: Not compatible with transformers library
- Unique quantization: Uses proprietary blocks/scales format
- 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
run_gpt_oss_inference.py
: Initial inference attemptsimple_gpt_oss_chat.py
: Simplified chat interfaceinspect_gpt_oss_weights.py
: Weight analysis toolmodel_inspection.json
: Complete weight mappinggpt_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
- Wait for official support: OpenAI might release proper inference code
- Community implementation: Someone might reverse-engineer the model
- Model conversion: Convert to standard formats (GGUF, ONNX)
- 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:
- Official implementation (not yet available)
- Alternative models (Mixtral, GPT-NeoX, etc.)
- 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