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Model: Shinzmann/naija-petro-8b-GGUF Source: Original Platform
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*.7z filter=lfs diff=lfs merge=lfs -text
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qwen3-8b.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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qwen3-8b.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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59
Modelfile
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FROM qwen3-8b.Q8_0.gguf
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TEMPLATE """{{- if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|im_start|>"
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PARAMETER temperature 0.6
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PARAMETER min_p 0.0
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PARAMETER top_k 20
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PARAMETER top_p 0.95
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PARAMETER repeat_penalty 1
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61
README.md
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---
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license: apache-2.0
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language:
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- en
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base_model: Shinzmann/naija-petro-8b
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pipeline_tag: text-generation
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tags:
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- petroleum-engineering
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- oil-and-gas
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- nigeria
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- qwen3
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- gguf
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- llama-cpp
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- ollama
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- quantized
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---
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# Naija-Petro 8B — GGUF
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GGUF quantizations of [`Shinzmann/naija-petro-8b`](https://huggingface.co/Shinzmann/naija-petro-8b) for CPU/edge inference with [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.com), LM Studio, and compatible runtimes.
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See the full [model card](https://huggingface.co/Shinzmann/naija-petro-8b) for training details, intended use, and limitations. For Nigeria-specific accuracy, use these weights with the [Naija-Petro RAG system](https://github.com/Mystique1337/naija-petro).
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## Available quantizations
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| File suffix | Method | Notes |
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|---|---|---|
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| `Q4_K_M` | 4-bit (k-quant, medium) | Best size/quality trade-off — recommended default |
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| `Q8_0` | 8-bit | Near-lossless; larger and slower |
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## Usage
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**Ollama**
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```bash
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ollama run hf.co/Shinzmann/naija-petro-8b-GGUF:Q4_K_M
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```
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**llama.cpp**
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```bash
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# download a specific quant, then:
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./llama-cli -hf Shinzmann/naija-petro-8b-GGUF:Q4_K_M \
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-p "Explain the material balance equation for an undersaturated reservoir." \
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-c 4096
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```
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**Python (llama-cpp-python)**
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="Shinzmann/naija-petro-8b-GGUF",
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filename="*Q4_K_M.gguf",
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n_ctx=4096,
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)
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print(llm.create_chat_completion(messages=[
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{"role": "system", "content": "You are Naija-Petro, an expert petroleum-engineering AI assistant."},
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{"role": "user", "content": "How do you interpret a Horner plot?"},
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])["choices"][0]["message"]["content"])
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```
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## License
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Apache-2.0 (inherited from Qwen3-8B). Validate outputs with qualified engineers before operational use.
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"torch_dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": 151669,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"unsloth_fixed": true,
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"unsloth_version": "2026.3.8",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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3
qwen3-8b.Q4_K_M.gguf
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qwen3-8b.Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:8ddd1f3555bb6ecb17b913424a2657edb76961106ee32778184acce9f32bc2f8
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size 5027784064
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qwen3-8b.Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9a989b478f1b6f160d424339fb227b99d864f51fb9151c770bcc88cbcbdf462
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size 8709518720
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