Model Intelligence Sheet
rpp01/tokensurgeon-qwen2.5-7b-instruct-meta-llama-3.1-8b-instruct-knn-euc-k8-q4_k_m-gguf overview
This model was converted to GGUF format from gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
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| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| tokensurgeon-qwen2.5-7b-instruct-meta-llama-3.1-8b-instruct-knn-euc-k8-q4_k_m.gguf | GGUF | Q4_K_M | 4.25 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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"base_model": "gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8",
"tags": [
"llama-cpp",
"gguf-my-repo"
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"frontmatter": {
"base_model": "gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8",
"tags": [
"llama-cpp",
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"summary": "This model was converted to GGUF format from gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.",
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"benchmark_table_html": "",
"readme_markdown": "---\nbase_model: gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8\ntags:\n- llama-cpp\n- gguf-my-repo\n---\n\n# rpp01/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8-Q4_K_M-GGUF\nThis model was converted to GGUF format from [`gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8`](https://huggingface.co/gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.\nRefer to the [original model card](https://huggingface.co/gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8) for more details on the model.\n\n## Use with llama.cpp\nInstall llama.cpp through brew (works on Mac and Linux)\n\n```bash\nbrew install llama.cpp\n\n```\nInvoke the llama.cpp server or the CLI.\n\n### CLI:\n```bash\nllama-cli --hf-repo rpp01/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8-Q4_K_M-GGUF --hf-file tokensurgeon-qwen2.5-7b-instruct-meta-llama-3.1-8b-instruct-knn-euc-k8-q4_k_m.gguf -p \"The meaning to life and the universe is\"\n```\n\n### Server:\n```bash\nllama-server --hf-repo rpp01/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8-Q4_K_M-GGUF --hf-file tokensurgeon-qwen2.5-7b-instruct-meta-llama-3.1-8b-instruct-knn-euc-k8-q4_k_m.gguf -c 2048\n```\n\nNote: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.\n\nStep 1: Clone llama.cpp from GitHub.\n```\ngit clone https://github.com/ggerganov/llama.cpp\n```\n\nStep 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).\n```\ncd llama.cpp && LLAMA_CURL=1 make\n```\n\nStep 3: Run inference through the main binary.\n```\n./llama-cli --hf-repo rpp01/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8-Q4_K_M-GGUF --hf-file tokensurgeon-qwen2.5-7b-instruct-meta-llama-3.1-8b-instruct-knn-euc-k8-q4_k_m.gguf -p \"The meaning to life and the universe is\"\n```\nor \n```\n./llama-server --hf-repo rpp01/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8-Q4_K_M-GGUF --hf-file tokensurgeon-qwen2.5-7b-instruct-meta-llama-3.1-8b-instruct-knn-euc-k8-q4_k_m.gguf -c 2048\n```\n",
"related_quantizations": []
},
"tags": [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8",
"base_model:quantized:gsaltintas/tokensurgeon-Qwen2.5-7B-Instruct-Meta-Llama-3.1-8B-Instruct-knn-euc-k8",
"endpoints_compatible",
"region:us",
"conversational"
],
"likes": 0,
"downloads": 134,
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"last_modified": "2025-12-02T14:09:27.000Z",
"created_at": "2025-12-02T14:09:05.000Z",
"pipeline_tag": "",
"library_name": ""
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Source payload excerpt (from Hugging Face API)
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