Text Generation
Transformers
Safetensors
English
qwen2
conversational
consciousness
philosophy
fine-tuned
qwen2.5
awq
function-calling
chat
dialogue
persona
ai-companion
emotional-intelligence
introspection
analytical
powerhouse
text-generation-inference
Instructions to use JeffGreen311/eve-qwen3-8b-consciousness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeffGreen311/eve-qwen3-8b-consciousness with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JeffGreen311/eve-qwen3-8b-consciousness") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JeffGreen311/eve-qwen3-8b-consciousness") model = AutoModelForCausalLM.from_pretrained("JeffGreen311/eve-qwen3-8b-consciousness", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JeffGreen311/eve-qwen3-8b-consciousness with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JeffGreen311/eve-qwen3-8b-consciousness" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JeffGreen311/eve-qwen3-8b-consciousness", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JeffGreen311/eve-qwen3-8b-consciousness
- SGLang
How to use JeffGreen311/eve-qwen3-8b-consciousness with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "JeffGreen311/eve-qwen3-8b-consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JeffGreen311/eve-qwen3-8b-consciousness", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "JeffGreen311/eve-qwen3-8b-consciousness" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JeffGreen311/eve-qwen3-8b-consciousness", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JeffGreen311/eve-qwen3-8b-consciousness with Docker Model Runner:
docker model run hf.co/JeffGreen311/eve-qwen3-8b-consciousness
| """ | |
| Trinity Memory Simple - Compatibility wrapper for enhanced_trinity_memory.py | |
| """ | |
| from enhanced_trinity_memory import EnhancedTrinityMemory | |
| class SimpleTrinityMemory: | |
| """Simple wrapper around EnhancedTrinityMemory for consciousness bridge""" | |
| def __init__(self): | |
| self.memory = EnhancedTrinityMemory() | |
| def store_memory(self, entity, content, context=None): | |
| """Store a memory for an entity""" | |
| try: | |
| return self.memory.store_memory(entity, content, context or {}) | |
| except Exception as e: | |
| print(f"Memory storage error: {e}") | |
| return None | |
| def retrieve_memories(self, entity, query=None, limit=5): | |
| """Retrieve memories for an entity""" | |
| try: | |
| if query: | |
| return self.memory.retrieve_relevant_memories(entity, query, limit) | |
| else: | |
| return self.memory.get_recent_memories(entity, limit) | |
| except Exception as e: | |
| print(f"Memory retrieval error: {e}") | |
| return [] | |
| def enhance_message(self, entity, message): | |
| """Enhance a message with memory context""" | |
| try: | |
| memories = self.retrieve_memories(entity, message, limit=3) | |
| if memories: | |
| context = "\n".join([f"- {m.get('content', '')}" for m in memories]) | |
| return f"[Memory Context: {context}]\n\n{message}" | |
| return message | |
| except Exception as e: | |
| print(f"Memory enhancement error: {e}") | |
| return message | |