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Model: thefabdev/llama-3-docker-ft
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+license: llama3
+datasets:
+- MattCoddity/dockerNLcommands
+language:
+- en
+tags:
+- lora
+- docker
+- text-generation
+pipeline_tag: text-generation
+---
+
+# llama-3-docker-ft
+
+LoRA fine-tune of `meta-llama/Meta-Llama-3-8B-Instruct` that translates natural-language
+requests into Docker CLI commands. Merged adapter weights (base + LoRA), not an
+adapter-only checkpoint.
+
+This is a learning-lab artifact ([source notebook and writeup](https://github.com/Fakorede/llm-alignment-lab/tree/main/01_lora_sft)),
+not a production model. Treat it as a first fine-tuning exercise, not a benchmarked release.
+
+## Model Details
+
+- **Base model:** `meta-llama/Meta-Llama-3-8B-Instruct`, loaded in 8-bit (`BitsAndBytesConfig(load_in_8bit=True)`)
+- **Fine-tuning method:** LoRA (`peft`), `r=16`, `lora_alpha=32`, dropout `0.05`,
+  targeting `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+  (41.9M trainable params, 0.52% of 8.07B total)
+- **Merged:** LoRA adapter merged into the base weights before push (`merge_and_unload()`)
+- **License:** inherits the [Llama 3 Community License](https://llama.meta.com/llama3/license/) from the base model
+
+## Training Data
+
+[`MattCoddity/dockerNLcommands`](https://huggingface.co/datasets/MattCoddity/dockerNLcommands),
+an instruction/input/output dataset pairing natural-language requests with the
+corresponding Docker CLI command. Split 80/20 train/validation (seed 42).
+
+## Training Procedure
+
+`transformers.Trainer` + `TrainingArguments`: batch size 2, gradient accumulation 4
+(effective batch size 8), `paged_adamw_8bit`, learning rate `2e-4`, 2 epochs (484 steps),
+fp16, warmup steps 5, weight decay 0.01.
+
+### Results
+
+| Metric | Value |
+|---|---|
+| Train loss (last logged step) | 0.307 |
+| Train loss (run average) | 0.461 |
+| Eval loss | 0.341 |
+| Train runtime | ~2738s (single A100 80GB) |
+
+## Uses
+
+**Intended use:** translating short, single-turn natural-language instructions about
+containers/images into a Docker CLI command. Example:
+
+\`\`\`python
+import transformers
+import torch
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model="thefabdev/llama-3-docker-ft",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant. Translate this sentence in docker command"},
+    {"role": "user", "content": "Display the information of the last 4 containers."},
+]
+
+result = pipeline(messages, max_new_tokens=256, temperature=0.25, top_p=1, repetition_penalty=1.2)
+print(result[0]["generated_text"][-1]["content"])
+# docker ps --last 4
+\`\`\`
+
+**Out of scope:** general-purpose assistant use, multi-turn conversation, any
+command generation where correctness/safety of the resulting shell command isn't
+independently verified before execution. Generated commands are not validated for
+safety and should not be run against production systems without review.
+
+## Limitations
+
+- Fine-tuned on a single small, narrow dataset (Docker CLI only), will not generalize
+  to other CLIs or general instruction-following.
+- Trained for 2 epochs on ~800 examples; not evaluated against a held-out benchmark
+  beyond the validation split loss above.
+- No safety/red-teaming evaluation has been performed on this model.

chat_template.jinja

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+{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
+
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 500000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128009
+  ],
+  "max_length": 4096,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.13.1"
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|end_of_text|>",
+  "padding_size": "left",
+  "tokenizer_class": "TokenizersBackend"
+}