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Model: Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758 Source: Original Platform
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phi-4.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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Modelfile
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Modelfile
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FROM phi-4.Q8_0.gguf
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TEMPLATE """{{ if .System }}<|im_start|><|system|><|im_sep|>{{ .System }}<|im_end|>{{ end }}{{ if .Prompt }}<|im_start|><|user|><|im_sep|>{{ .Prompt }}<|im_end|>{{ end }}<|im_start|><|assistant|><|im_sep|>{{ .Response }}<|im_end|>"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|im_start|>"
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PARAMETER stop "<|im_sep|>"
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PARAMETER temperature 1.5
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PARAMETER min_p 0.1
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README.md
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README.md
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---
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pipeline_tag: text-generation
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tags:
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- gguf
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- llama.cpp
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- unsloth
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- conversational
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base_model:
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- unsloth/Phi-4-unsloth-bnb-4bit
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datasets:
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- Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl
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---
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# joke-finetome-model-gguf-phi4-20260112-081758 : GGUF
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This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
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**Example usage**:
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- For text only LLMs: `./llama.cpp/llama-cli -hf Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758 --jinja`
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- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758 --jinja`
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## Available Model files:
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- `phi-4.Q8_0.gguf`
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## Ollama
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An Ollama Modelfile is included for easy deployment.
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This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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## Training artifacts
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- Plot (interactive): [`reports/training_loss_step.html`](reports/training_loss_step.html)
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- Run manifest: [`reports/run_manifest.json`](reports/run_manifest.json)
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- Inference sample: [`reports/inference_sample.json`](reports/inference_sample.json)
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- Config snapshot: [`reports/config_snapshot.json`](reports/config_snapshot.json)
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## Inference
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This repository contains a **GGUF** model intended to be used with **llama.cpp** and/or deployed on **Hugging Face Inference Endpoints (llama.cpp container)**.
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Recommended Inference Endpoints knobs:
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- Max tokens / request: **1024**
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- Max concurrent requests: **2**
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### Local llama.cpp (Phi-4 template)
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```bash
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llama-cli -hf Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758:q8_0 -cnv --chat-template phi4
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```
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### Hugging Face Inference Endpoint (llama.cpp)
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When creating an endpoint, select this repo and the GGUF file **<your_model>.gguf** (quant: **q8_0**).
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Recommended settings are stored in: `inference/endpoint_recipe.json`.
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Python client example: `inference/hf_endpoint_client.py`
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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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"LlamaForCausalLM"
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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": 100257,
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"torch_dtype": "bfloat16",
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"eos_token_id": 100265,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 17920,
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"max_position_embeddings": 16384,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 10,
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"original_max_position_embeddings": 16384,
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"pad_token_id": 100351,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 250000,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"unsloth_fixed": true,
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"unsloth_version": "2026.1.2",
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"use_cache": true,
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"vocab_size": 100352
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}
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inference/endpoint_recipe.json
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inference/endpoint_recipe.json
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{
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"engine": "llama.cpp",
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"recommended_endpoint_settings": {
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"max_tokens_per_request": 1024,
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"max_concurrent_requests": 2,
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"notes": "Memory scales roughly with (max_concurrent_requests * max_tokens_per_request)."
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},
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"recommended_generation_defaults": {
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"temperature": 1.2,
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"top_p": 0.95,
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"min_p": 0.05,
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"repeat_penalty": 1.08,
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"max_tokens": 2560
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},
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"chat_template": "phi4",
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"gguf_file": "",
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"gguf_quant": "q8_0"
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}
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inference/hf_endpoint_client.py
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inference/hf_endpoint_client.py
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import os
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from huggingface_hub import InferenceClient
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# Required env vars:
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# export HF_TOKEN="..."
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# export HF_ENDPOINT_BASE_URL="https://xxxx.endpoints.huggingface.cloud"
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client = InferenceClient(
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base_url=os.environ["HF_ENDPOINT_BASE_URL"],
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api_key=os.environ["HF_TOKEN"],
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)
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resp = client.chat.completions.create(
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Write a short joke in the style of The Office."},
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],
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max_tokens=2560,
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temperature=1.2,
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top_p=0.95,
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)
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print(resp.choices[0].message.content)
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inference/llama_cli_examples.md
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inference/llama_cli_examples.md
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### Local inference (llama.cpp)
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```bash
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llama-cli -hf {REPO_ID}:q8_0 -cnv --chat-template phi4
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```
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### Server (OpenAI-compatible)
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```bash
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llama-server -hf {REPO_ID}:q8_0
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# /v1/chat/completions will be available (OpenAI-compatible)
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```
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phi-4.Q8_0.gguf
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phi-4.Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:70be651211a3ca863ad8bc6c9b2596bf131f434686a5d06808c79bad7e5743b8
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size 15580507200
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reports/config_snapshot.json
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reports/config_snapshot.json
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{
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"MODEL_NAME": "unsloth/Phi-4-unsloth-bnb-4bit",
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"CHAT_TEMPLATE": "phi-4",
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"MAX_SEQ_LENGTH": 2048,
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"LOAD_IN_4BIT": true,
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"DATASET_NAME": "Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl",
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"DATASET_SPLIT": "train",
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"PER_DEVICE_TRAIN_BATCH_SIZE": 2,
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"GRADIENT_ACCUMULATION_STEPS": 4,
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"WARMUP_STEPS": 10,
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"MAX_STEPS": 2000,
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"LEARNING_RATE": 9.95267419777795e-06,
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"LR_AUTO_ENABLED": true,
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"LR_AUTO_USE_N": "train",
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"LR_AUTO_N_REF": 1436,
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"LR_AUTO_BASE": 1e-05,
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"LR_AUTO_MULT": 0.5,
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"LR_AUTO_FINAL": 5e-06,
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"WEIGHT_DECAY": 0.009206070410847844,
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"LR_SCHEDULER_TYPE": "linear",
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"SEED": 3407,
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"PLOTLY_DARK_MODE": true,
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"PLOTLY_BASE_COLOR": "#00CC96",
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"PLOTLY_EMA_SPAN": 25,
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"HF_REPO_ID_MERGED_RESOLVED": "Mathieu-Thomas-JOSSET/joke-20260112-081758",
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"HF_REPO_ID_GGUF_RESOLVED": "Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758",
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"HF_ARTIFACTS_DIR_IN_REPO": "reports"
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}
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reports/inference_sample.json
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reports/inference_sample.json
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{
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"source": "dataset",
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"index": 232,
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"messages": [
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{
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"role": "user",
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"content": "Dwight: \"Oh, man.\"\nMichael: \"How did we do it?\"\nDwight: \"I don’t … have no idea.\""
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}
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]
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}
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reports/report.html
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reports/report.html
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reports/run_manifest.json
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reports/run_manifest.json
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{
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"started_at": "2026-01-12T08:52:02+01:00",
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"repos": {
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"merged": "Mathieu-Thomas-JOSSET/joke-20260112-081758",
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"gguf": "Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758"
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},
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"model_name": "unsloth/Phi-4-unsloth-bnb-4bit",
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"dataset": {
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"name": "Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl",
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"split": "train"
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},
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"training": {
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"max_steps": 2000,
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"learning_rate": 9.95267419777795e-06,
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"per_device_train_batch_size": 2,
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"gradient_accumulation_steps": 4,
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"max_seq_length": 2048,
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"seed": 3407,
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"optimizer": "adamw_8bit",
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"lr_scheduler_type": "linear"
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},
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"auto_lr": {
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"enabled": true,
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"use_n": "train",
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"n_ref": 1436,
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"base": 1e-05,
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"mult": 0.5,
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"final": 5e-06
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},
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"metrics": {
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"train_runtime": 2220.807,
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"train_samples_per_second": 5.944,
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"train_steps_per_second": 0.743,
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"total_flos": 4.35104765343744e+16,
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"train_loss": 1.654274252106746,
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"epoch": 3.3342618384401113
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},
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"best": {
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"checkpoint": "/content/outputs/continue_r1_from_350_20260112_073729/checkpoint-100",
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"metric": 2.2380564212799072,
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"metric_name": "eval_loss"
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},
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"plotly": {
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"html": "reports/training_loss_step.html",
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"png": null
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},
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"inference_sample": {
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"source": "dataset",
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"index": 232,
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"messages": [
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{
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"role": "user",
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"content": "Dwight: \"Oh, man.\"\nMichael: \"How did we do it?\"\nDwight: \"I don’t … have no idea.\""
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}
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]
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}
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}
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14
reports/training_loss_step.html
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reports/training_loss_step.html
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