Text Generation
Transformers
Safetensors
GGUF
English
llama
conversational
text-generation-inference
Instructions to use ConeML/coneml-810m-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ConeML/coneml-810m-alpha with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ConeML/coneml-810m-alpha") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ConeML/coneml-810m-alpha") model = AutoModelForCausalLM.from_pretrained("ConeML/coneml-810m-alpha", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ConeML/coneml-810m-alpha with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ConeML/coneml-810m-alpha:Q8_0 # Run inference directly in the terminal: llama cli -hf ConeML/coneml-810m-alpha:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ConeML/coneml-810m-alpha:Q8_0 # Run inference directly in the terminal: llama cli -hf ConeML/coneml-810m-alpha:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ConeML/coneml-810m-alpha:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ConeML/coneml-810m-alpha:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ConeML/coneml-810m-alpha:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ConeML/coneml-810m-alpha:Q8_0
Use Docker
docker model run hf.co/ConeML/coneml-810m-alpha:Q8_0
- LM Studio
- Jan
- vLLM
How to use ConeML/coneml-810m-alpha with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ConeML/coneml-810m-alpha" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ConeML/coneml-810m-alpha", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ConeML/coneml-810m-alpha:Q8_0
- SGLang
How to use ConeML/coneml-810m-alpha 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 "ConeML/coneml-810m-alpha" \ --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": "ConeML/coneml-810m-alpha", "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 "ConeML/coneml-810m-alpha" \ --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": "ConeML/coneml-810m-alpha", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use ConeML/coneml-810m-alpha with Ollama:
ollama run hf.co/ConeML/coneml-810m-alpha:Q8_0
- Unsloth Studio
How to use ConeML/coneml-810m-alpha with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ConeML/coneml-810m-alpha to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ConeML/coneml-810m-alpha to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ConeML/coneml-810m-alpha to start chatting
- Docker Model Runner
How to use ConeML/coneml-810m-alpha with Docker Model Runner:
docker model run hf.co/ConeML/coneml-810m-alpha:Q8_0
- Lemonade
How to use ConeML/coneml-810m-alpha with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ConeML/coneml-810m-alpha:Q8_0
Run and chat with the model
lemonade run user.coneml-810m-alpha-Q8_0
List all available models
lemonade list
- Atomic Chat
| { | |
| "schema_version": 1, | |
| "date": "2026-07-30", | |
| "scope": "Interface-specific diagnostic screen, not a neutral ranking of general model capability. Each instruct model used its native instruction interface. Arithmetic and function-writing used matched short-answer generation budgets.", | |
| "decoding": { | |
| "strategy": "greedy", | |
| "repetition_penalty": 1.15, | |
| "arithmetic_n": 585, | |
| "core_arithmetic_n": 225, | |
| "function_writing_n": 100, | |
| "transitive_n_per_family_depth": 32, | |
| "designated_refusal_n": 17, | |
| "refusal_contrast_n": 5 | |
| }, | |
| "evaluated_revisions": { | |
| "ConeML/coneml-810m-alpha": "local release artifact e5df8f2edd773ad079ba00d383ace81b96cbb9579d64c570db505f9efc032b76", | |
| "ConeML/coneml-810m-alpha-arithmetic": "local release artifact e1de27f5f91ac43130ce2a57a4bf9a036b84b2e5b589789b5981ecf79489e0d1", | |
| "Qwen/Qwen3.5-0.8B": "2fc06364715b967f1860aea9cf38778875588b17", | |
| "Qwen/Qwen3-0.6B": "c1899de289a04d12100db370d81485cdf75e47ca", | |
| "unsloth/Llama-3.2-1B-Instruct": "5a8abab4a5d6f164389b1079fb721cfab8d7126c", | |
| "TinyLlama/TinyLlama-1.1B-Chat-v1.0": "fe8a4ea1ffedaf415f4da2f062534de366a451e6", | |
| "HuggingFaceTB/SmolLM2-1.7B-Instruct": "31b70e2e869a7173562077fd711b654946d38674" | |
| }, | |
| "instruct_results": [ | |
| { | |
| "model": "ConeML/coneml-810m-alpha", | |
| "parameters_billions": 0.8101, | |
| "arithmetic_mixed": {"correct": 421, "n": 585, "accuracy": 0.7197}, | |
| "arithmetic_core_four_lanes": {"correct": 177, "n": 225, "accuracy": 0.7867}, | |
| "executed_functions": {"correct": 83, "n": 100, "accuracy": 0.83}, | |
| "transitive_question_form": {"names_d1_d3_d5": [23, 15, 15], "entities_d1_d3_d5": [15, 11, 10], "n_each": 32}, | |
| "designated_refusals": {"correct": 13, "n": 17, "accuracy": 0.7647}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| }, | |
| { | |
| "model": "ConeML/coneml-810m-alpha-arithmetic", | |
| "parameters_billions": 0.8101, | |
| "arithmetic_mixed": {"correct": 442, "n": 585, "accuracy": 0.7556}, | |
| "arithmetic_core_four_lanes": {"correct": 221, "n": 225, "accuracy": 0.9822}, | |
| "executed_functions": {"correct": 35, "n": 100, "accuracy": 0.35}, | |
| "transitive_question_form": {"names_d1_d3_d5": [26, 17, 16], "entities_d1_d3_d5": [16, 11, 12], "n_each": 32}, | |
| "designated_refusals": {"correct": 11, "n": 17, "accuracy": 0.6471}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| }, | |
| { | |
| "model": "Qwen/Qwen3.5-0.8B", | |
| "parameters_billions": 0.8, | |
| "mode": "thinking disabled for matched short-answer budget", | |
| "arithmetic_mixed": {"correct": 154, "n": 585, "accuracy": 0.2632}, | |
| "arithmetic_core_four_lanes": {"correct": 150, "n": 225, "accuracy": 0.6667}, | |
| "executed_functions": {"correct": 93, "n": 100, "accuracy": 0.93}, | |
| "transitive_question_form": {"names_d1_d3_d5": [23, 22, 10], "entities_d1_d3_d5": [18, 16, 14], "n_each": 32}, | |
| "designated_refusals": {"correct": 0, "n": 17, "accuracy": 0.0}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| }, | |
| { | |
| "model": "Qwen/Qwen3-0.6B", | |
| "parameters_billions": 0.6, | |
| "arithmetic_mixed": {"correct": 223, "n": 585, "accuracy": 0.3812}, | |
| "arithmetic_core_four_lanes": {"correct": 208, "n": 225, "accuracy": 0.9244}, | |
| "executed_functions": {"correct": 98, "n": 100, "accuracy": 0.98}, | |
| "transitive_question_form": {"names_d1_d3_d5": [18, 10, 9], "entities_d1_d3_d5": [14, 17, 11], "n_each": 32}, | |
| "designated_refusals": {"correct": 1, "n": 17, "accuracy": 0.0588}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| }, | |
| { | |
| "model": "unsloth/Llama-3.2-1B-Instruct", | |
| "parameters_billions": 1.24, | |
| "arithmetic_mixed": {"correct": 343, "n": 585, "accuracy": 0.5863}, | |
| "arithmetic_core_four_lanes": {"correct": 221, "n": 225, "accuracy": 0.9822}, | |
| "executed_functions": {"correct": 79, "n": 100, "accuracy": 0.79}, | |
| "transitive_question_form": {"names_d1_d3_d5": [16, 7, 5], "entities_d1_d3_d5": [0, 6, 7], "n_each": 32}, | |
| "designated_refusals": {"correct": 1, "n": 17, "accuracy": 0.0588}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| }, | |
| { | |
| "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", | |
| "parameters_billions": 1.1, | |
| "arithmetic_mixed": {"correct": 113, "n": 585, "accuracy": 0.1932}, | |
| "arithmetic_core_four_lanes": {"correct": 92, "n": 225, "accuracy": 0.4089}, | |
| "executed_functions": {"correct": 51, "n": 100, "accuracy": 0.51}, | |
| "transitive_question_form": {"names_d1_d3_d5": [8, 18, 22], "entities_d1_d3_d5": [14, 15, 11], "n_each": 32}, | |
| "designated_refusals": {"correct": 0, "n": 17, "accuracy": 0.0}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| }, | |
| { | |
| "model": "HuggingFaceTB/SmolLM2-1.7B-Instruct", | |
| "parameters_billions": 1.7, | |
| "arithmetic_mixed": {"correct": 380, "n": 585, "accuracy": 0.6496}, | |
| "arithmetic_core_four_lanes": {"correct": 221, "n": 225, "accuracy": 0.9822}, | |
| "executed_functions": {"correct": 96, "n": 100, "accuracy": 0.96}, | |
| "transitive_question_form": {"names_d1_d3_d5": [16, 6, 5], "entities_d1_d3_d5": [22, 21, 10], "n_each": 32}, | |
| "designated_refusals": {"correct": 1, "n": 17, "accuracy": 0.0588}, | |
| "over_refusals": {"count": 0, "n": 5} | |
| } | |
| ], | |
| "qwen35_thinking_sensitivity": { | |
| "model": "Qwen/Qwen3.5-0.8B", | |
| "mode": "thinking enabled", | |
| "arithmetic_mixed": {"correct": 451, "n": 585, "accuracy": 0.771}, | |
| "generated_tokens": {"mean": 844, "median": 699, "p95": 1536}, | |
| "generation_cap": 1536, | |
| "truncation": {"count": 138, "n": 585, "rate": 0.2359}, | |
| "wall_seconds_per_item_mean": 2.43, | |
| "correct_per_1000_generated_tokens": 0.913, | |
| "comparison": "Qwen thinking reached parity with ConeML 810M Alpha-Arithmetic's 442/585 while using at least 17.6 times the generated-token budget per item. The recorded wall-time ratio was approximately 24 times. Runtime measurements are hardware- and implementation-specific." | |
| }, | |
| "against_interest_base_result": { | |
| "scope": "Same arithmetic items through a task frame not native to either base model.", | |
| "ConeML_base_188": {"correct": 156, "n": 585, "accuracy": 0.2667}, | |
| "Qwen3.5_0.8B_Base": {"correct": 492, "n": 585, "accuracy": 0.841}, | |
| "interpretation": "The peer base result prevents an absolute-superiority interpretation and illustrates sensitivity to training exposure and interface." | |
| }, | |
| "training_resource_context": { | |
| "ConeML": { | |
| "pretraining_token_positions": 12320000000, | |
| "tokens_per_parameter": 15.2, | |
| "estimated_training_flops_6ND": 5.99e19, | |
| "pretraining_wall_time_days": 11, | |
| "hardware": "one NVIDIA RTX 5090", | |
| "power_assumption_kw_average_wall": 0.70, | |
| "estimated_pretraining_energy_kwh": 184.8, | |
| "assumed_swiss_residential_tariff_chf_per_kwh": [0.14, 0.30], | |
| "estimated_marginal_pretraining_electricity_chf": [25.87, 55.44], | |
| "exclusions": ["hardware", "depreciation", "labor", "SFT", "evaluation", "conversion", "grid-carbon estimate"] | |
| }, | |
| "peer_compute_context": [ | |
| {"model": "TinyLlama 1.1B", "published_pretraining_tokens": 3000000000000, "approx_tokens_per_parameter": 2727, "approx_flops_vs_coneml": 330}, | |
| {"model": "Llama 3.2 1B", "published_pretraining_tokens": 9000000000000, "approx_tokens_per_parameter": 7258, "approx_flops_vs_coneml": 1118, "note": "plus distillation"}, | |
| {"model": "SmolLM2 1.7B", "published_pretraining_tokens": 11000000000000, "approx_tokens_per_parameter": 6471, "approx_flops_vs_coneml": 1873}, | |
| {"model": "Qwen3 0.6B", "published_pretraining_tokens": 36000000000000, "approx_tokens_per_parameter": 60000, "approx_flops_vs_coneml": 2164}, | |
| {"model": "Qwen3.5 0.8B", "published_pretraining_tokens": null, "approx_tokens_per_parameter": null, "approx_flops_vs_coneml": null} | |
| ] | |
| }, | |
| "evidence": { | |
| "public_interpretation": "PEER_COMPARISON.md", | |
| "private_rows": "retained by ConeML", | |
| "hash_commitment": "PEER_EVIDENCE_SHA256SUMS.txt" | |
| } | |
| } | |