Image-Text-to-Text
GGUF
English
Chinese
quantized
Mixture of Experts
qwen3_5_moe
tool-use
imatrix
conversational
Instructions to use Accio-Lab/occamy-1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Accio-Lab/occamy-1.0-GGUF 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 Accio-Lab/occamy-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use Accio-Lab/occamy-1.0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Accio-Lab/occamy-1.0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Accio-Lab/occamy-1.0-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M
- Ollama
How to use Accio-Lab/occamy-1.0-GGUF with Ollama:
ollama run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Accio-Lab/occamy-1.0-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Accio-Lab/occamy-1.0-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Accio-Lab/occamy-1.0-GGUF with Docker Model Runner:
docker model run hf.co/Accio-Lab/occamy-1.0-GGUF:Q4_K_M
- Lemonade
How to use Accio-Lab/occamy-1.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.occamy-1.0-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Accio-Lab/occamy-1.0-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Accio-Lab/occamy-1.0-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Accio-Lab/occamy-1.0-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download validation/results.json from Accio-Lab/occamy-1.0-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 109 kB
-
https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/resolve/main/validation/results.json
- Command line
-
hf download hf://Accio-Lab/occamy-1.0-GGUF/validation/results.json
-
curl -L -o results.json https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/resolve/main/validation/results.json
109 kB
| { | |
| "schema_version": 1, | |
| "generated_utc": "2026-09-17T23:56:57.083966+00:00", | |
| "scope": "BF16 reference and twelve concrete GGUF quantization artifacts; small fixed screening subsets", | |
| "source": { | |
| "repo": "Accio-Lab/occamy-1.0", | |
| "revision": "8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8" | |
| }, | |
| "runtime": { | |
| "engine": "llama.cpp", | |
| "commit": "972d2313bc0bf0a45f634f77d95c9fb03aeab12c", | |
| "hardware": "NVIDIA B200", | |
| "gpus_per_job": 1, | |
| "mtp": false, | |
| "cpu_threads_per_job": 8, | |
| "flash_attention": true, | |
| "kv_cache_type": "f16", | |
| "batch": 2048, | |
| "microbatch": 512, | |
| "engine_repetitions": 5, | |
| "engine_warmup": true | |
| }, | |
| "tokenizer_compatibility": { | |
| "benchmark_effective_pretokenizer": "qwen35", | |
| "comparison_reference": "GGUF BF16", | |
| "hf_source_behavior": "NFC normalization plus qwen2 tokenization", | |
| "hf_source_equivalence": "Mismatch observed on small probes; these benchmarks do not establish equivalence to the HF source tokenizer", | |
| "publication_gate": "ten_corrected_copies_verified_see_release_json; Q4_K_M/Q8_0 unchanged; BF16 reference only" | |
| }, | |
| "quality_method": { | |
| "cases_per_model": 60, | |
| "math_cases": 24, | |
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| "json_cases": 6, | |
| "tool_cases": 6, | |
| "temperature": 0, | |
| "seed": 20260918, | |
| "max_generated_tokens": 512, | |
| "thinking": false, | |
| "retries": 0, | |
| "per_model_concurrency": 1, | |
| "selection_algorithm": "sort SHA256(\"20260918:\" + stable ID), take first 24 for each math/code suite", | |
| "frozen_data_sha256": "8c587abf50aa5b06b379ff83070f0c56e9eb889dd1b1073a9fb3834677888f63", | |
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| "evalplus_revision": "26d6d00bb1fd0fa37f39c99d5290da67891d1c5e", | |
| "code_scoring": "Pinned EvalPlus sanitizer and base + extended tests; actual execution in an isolated, network-disabled, resource-limited CPU sandbox", | |
| "strict_json": "Parse the complete response without stripping Markdown fences", | |
| "tool_scoring": "Expected native tool name and argument structure; first tool call only" | |
| }, | |
| "ppl_method": { | |
| "dataset": "Salesforce/wikitext", | |
| "configuration": "wikitext-2-raw-v1", | |
| "split": "test", | |
| "revision": "b08601e04326c79dfdd32d625aee71d232d685c3", | |
| "chunks": 32, | |
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| "direct_bf16_ppl": 6.2385, | |
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| "bf16_self_replay_ppl": 6.23851, | |
| "decoded_dump_base_ppl": 6.232933, | |
| "dump_floor_delta": 0.005577 | |
| }, | |
| "limitations": [ | |
| "These are fixed small subsets, not full official GSM8K/HumanEval+ results; small score differences do not establish a ranking.", | |
| "The math prompt did not ask for ####, so strict extraction failure is not synonymous with wrong arithmetic. Strict and upstream flexible scores are separate.", | |
| "Truncated outputs remain in denominators. A passed inference job does not imply a correct answer.", | |
| "Engine throughput excludes tokenization and sampling. End-to-end tests use six requests repeated three times with cache disabled.", | |
| "Three additional artifacts used concurrent single-GPU jobs across four cards. Physical-card variation and concurrent CPU/IO load confound comparison with the initial single-card runs.", | |
| "Ten newly generated types share the same BF16 input and imatrix. Reused Q4_K_M/Q8_0 used different historical conversion recipes; this is not a bit-width-only controlled experiment.", | |
| "Benchmark tokenization uses qwen35 to align with GGUF BF16. Source-tokenizer probes found NFC plus qwen2 behavior and disagreement on small inputs. GGUF-reference alignment does not establish HF-source equivalence. Reused Q4/Q8 runtime overrides do not modify artifact bytes; release copies have corrected metadata and bounded validation recorded in release.json; this original benchmark protocol is unchanged.", | |
| "Lower-bit PPL degradation is a quantization quality tradeoff, not detected file damage: newly generated artifacts passed full finite scans and load tests. These checks do not establish BF16-equivalent quality.", | |
| "Direct BF16 PPL is rounded to four decimals. Native dump-relative PPL/KL metrics use a lossy reference and retain their numerical floor.", | |
| "Memory values are sampled device/process-tree peaks and lower bounds; file size is not memory usage." | |
| ], | |
| "models": [ | |
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| "type": "BF16", | |
| "artifact": { | |
| "filename": "occamy-1.0-BF16.gguf", | |
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| "sha256": "bbad6c5aaf28f20a700748613f998a2c0a98b6df94766ec37bf5c4a2cbf2aadf" | |
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| "new_quantization_tensor_count": null, | |
| "publication_gate": "reference_only_not_for_publication" | |
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| "measurement_cohort": "initial_single_gpu_jobs", | |
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| "pp2048": { | |
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| "memory": { | |
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| "file_size_is_not_runtime_memory": true | |
| } | |
| }, | |
| { | |
| "type": "IQ2_M", | |
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| "publication_gate": "corrected_copy_verified_see_release_json" | |
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| "tg128_d512": { | |
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| }, | |
| "ppl": { | |
| "ppl": 7.444683, | |
| "direct_bf16_reference_ppl": 6.2385, | |
| "ppl_delta_vs_direct_bf16": 1.2061830000000002, | |
| "dump_base_ppl": 6.232933, | |
| "paired_ppl_delta": 1.21175, | |
| "mean_kl": 0.294021, | |
| "same_top_percent": 78.407, | |
| "chunks": 32, | |
| "n_ctx": 512, | |
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| } | |