update preferences prefer uv

trl/SKILL.md

@@ -40,7 +40,7 @@ Use this skill when users want to:
 
 When assisting with training jobs:
 
-1. **Submit jobs directly with inline scripts** - The `script` parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to `hf_jobs()`. If user asks to "train a model", "fine-tune", or similar requests, you MUST create the training script AND submit the job immediately.
+1. **ALWAYS use `hf_jobs()` MCP tool** - Submit jobs using `hf_jobs("uv", {...})`, NOT bash `trl-jobs` commands. The `script` parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to `hf_jobs()`. If user asks to "train a model", "fine-tune", or similar requests, you MUST create the training script AND submit the job immediately using `hf_jobs()`.
 
 2. **Always include Trackio** - Every training script should include Trackio for real-time monitoring. Use example scripts in `scripts/` as templates.
 
@@ -116,27 +116,9 @@ The job is running in the background. Ask me to check status/logs when ready!
 
 ## Quick Start: Three Approaches
 
-### Approach 1: TRL Jobs Package (Easiest—Recommended for Beginners)
+### Approach 1: UV Scripts (Recommended—Default Choice)
 
-The `trl-jobs` package provides optimized defaults and one-liner training:
-
-```bash
-# Install (users only, not needed for this environment)
-pip install trl-jobs
-
-# Train with SFT (simplest possible)
-trl-jobs sft \
-  --model_name Qwen/Qwen2.5-0.5B \
-  --dataset_name trl-lib/Capybara
-```
-
-**Benefits:** Pre-configured settings, automatic Trackio integration, automatic Hub push, one-line commands
-**When to use:** User is new to training, standard scenarios, quick experimentation
-**Repository:** https://github.com/huggingface/trl-jobs
-
-### Approach 2: UV Scripts (Recommended for Custom Training)
-
-UV scripts use PEP 723 inline dependencies for clean, self-contained training. **Submit script content directly inline:**
+UV scripts use PEP 723 inline dependencies for clean, self-contained training. **This is the primary approach for Claude Code.**
 
 ```python
 hf_jobs("uv", {
@@ -183,11 +165,50 @@ trackio.finish()
 })
 ```
 
-**Benefits:** Clean code, dependencies declared inline (PEP 723), no file saving required
-**When to use:** Custom training logic, full control over training
-**See:** `references/uv_scripts_guide.md` for complete UV scripts guide
+**Benefits:** Direct MCP tool usage, clean code, dependencies declared inline (PEP 723), no file saving required, full control
+**When to use:** Default choice for all training tasks in Claude Code, custom training logic, any scenario requiring `hf_jobs()`
+
+#### Working with Scripts
+
+⚠️ **Important:** The `script` parameter accepts either inline code (as shown above) OR a URL. **Local file paths do NOT work.**
+
+**Why local paths don't work:**
+Jobs run in isolated Docker containers without access to your local filesystem. Scripts must be:
+- Inline code (recommended for custom training)
+- Publicly accessible URLs
+- Private repo URLs (with HF_TOKEN)
+
+**Common mistakes:**
+```python
+# ❌ These will all fail
+hf_jobs("uv", {"script": "train.py"})
+hf_jobs("uv", {"script": "./scripts/train.py"})
+hf_jobs("uv", {"script": "/path/to/train.py"})
+```
+
+**Correct approaches:**
+```python
+# ✅ Inline code (recommended)
+hf_jobs("uv", {"script": "# /// script\n# dependencies = [...]\n# ///\n\n<your code>"})
+
+# ✅ From Hugging Face Hub
+hf_jobs("uv", {"script": "https://huggingface.co/user/repo/resolve/main/train.py"})
+
+# ✅ From GitHub
+hf_jobs("uv", {"script": "https://raw.githubusercontent.com/user/repo/main/train.py"})
+
+# ✅ From Gist
+hf_jobs("uv", {"script": "https://gist.githubusercontent.com/user/id/raw/train.py"})
+```
+
+**To use local scripts:** Upload to HF Hub first:
+```bash
+huggingface-cli repo create my-training-scripts --type model
+huggingface-cli upload my-training-scripts ./train.py train.py
+# Use: https://huggingface.co/USERNAME/my-training-scripts/resolve/main/train.py
+```
 
-### Approach 3: TRL Maintained Scripts (Run Official Examples)
+### Approach 2: TRL Maintained Scripts (Official Examples)
 
 TRL provides battle-tested scripts for all methods. Can be run from URLs:
 
@@ -225,6 +246,26 @@ hub_repo_details(["uv-scripts/classification"], repo_type="dataset", include_rea
 
 **Popular collections:** ocr, classification, synthetic-data, vllm, dataset-creation
 
+### Approach 3: TRL Jobs Package (For Terminal Use)
+
+The `trl-jobs` package provides optimized defaults and one-liner training. **Note: This approach uses bash commands, not `hf_jobs()` MCP tool.**
+
+```bash
+# Install (users only, not needed for this environment)
+pip install trl-jobs
+
+# Train with SFT (simplest possible)
+trl-jobs sft \
+  --model_name Qwen/Qwen2.5-0.5B \
+  --dataset_name trl-lib/Capybara
+```
+
+**Benefits:** Pre-configured settings, automatic Trackio integration, automatic Hub push, one-line commands
+**When to use:** User working in terminal directly (not Claude Code context), quick local experimentation
+**Repository:** https://github.com/huggingface/trl-jobs
+
+⚠️ **In Claude Code context, use Approach 1 (UV Scripts) with `hf_jobs()` instead.**
+
 ## Hardware Selection
 
 | Model Size | Recommended Hardware | Cost (approx/hr) | Use Case |
@@ -545,7 +586,6 @@ Add to PEP 723 header:
 - `references/training_patterns.md` - Common training patterns and examples
 - `references/gguf_conversion.md` - Complete GGUF conversion guide
 - `references/trackio_guide.md` - Trackio monitoring setup
-- `references/uv_scripts_guide.md` - Complete UV scripts guide
 - `references/hardware_guide.md` - Hardware specs and selection
 - `references/hub_saving.md` - Hub authentication troubleshooting
 - `references/troubleshooting.md` - Common issues and solutions
@@ -577,7 +617,7 @@ Add to PEP 723 header:
 4. **Always enable Hub push** - Environment is ephemeral; without push, all results lost
 5. **Include Trackio** - Use example scripts as templates for real-time monitoring
 6. **Offer cost estimation** - When parameters are known, use `scripts/estimate_cost.py`
-7. **Three approaches available:** TRL Jobs package (easiest), UV scripts (custom, modern), TRL maintained scripts (official examples)
+7. **Use UV scripts (Approach 1)** - Default to `hf_jobs("uv", {...})` with inline scripts; TRL maintained scripts for standard training; avoid bash `trl-jobs` commands in Claude Code
 8. **Use hf_doc_fetch/hf_doc_search** for latest TRL documentation
 9. **Validate dataset format** before training with dataset inspector (see Dataset Validation section)
 10. **Choose appropriate hardware** for model size; use LoRA for models >7B

trl/references/troubleshooting.md

@@ -257,7 +257,7 @@ If issues persist:
 3. **Review related guides:**
    - `references/hub_saving.md` - Hub authentication issues
    - `references/hardware_guide.md` - Hardware selection and specs
-   - `references/uv_scripts_guide.md` - UV script format issues
    - `references/training_patterns.md` - Eval dataset requirements
+   - SKILL.md "Working with Scripts" section - Script format and URL issues
 
 4. **Ask in HF forums:** https://discuss.huggingface.co/

trl/references/uv_scripts_guide.md

@@ -1,414 +0,0 @@
-# UV Scripts Guide for TRL Training
-
-UV scripts are self-contained Python scripts with inline dependency declarations (PEP 723). They're the modern, recommended approach for custom TRL training.
-
-## What are UV Scripts?
-
-UV scripts declare dependencies at the top of the file using special comment syntax:
-
-```python
-# /// script
-# dependencies = [
-#     "trl>=0.12.0",
-#     "transformers>=4.36.0",
-# ]
-# ///
-
-# Your training code here
-from trl import SFTTrainer
-```
-
-## Benefits
-
-1. **Self-contained**: Dependencies are part of the script
-2. **Version control**: Pin exact versions for reproducibility
-3. **No setup files**: No requirements.txt or setup.py needed
-4. **Portable**: Run anywhere UV is installed
-5. **Clean**: Much cleaner than bash + pip + python strings
-
-## Creating a UV Script
-
-### Step 1: Define Dependencies
-
-Start with dependency declaration:
-
-```python
-# /// script
-# dependencies = [
-#     "trl>=0.12.0",              # TRL for training
-#     "transformers>=4.36.0",     # Transformers library
-#     "datasets>=2.14.0",         # Dataset loading
-#     "accelerate>=0.24.0",       # Distributed training
-#     "peft>=0.7.0",              # LoRA/PEFT (optional)
-# ]
-# ///
-```
-
-### Step 2: Add Training Code
-
-```python
-# /// script
-# dependencies = ["trl", "peft"]
-# ///
-
-from datasets import load_dataset
-from peft import LoraConfig
-from trl import SFTTrainer, SFTConfig
-
-# Load dataset
-dataset = load_dataset("trl-lib/Capybara", split="train")
-
-# Configure training
-config = SFTConfig(
-    output_dir="my-model",
-    num_train_epochs=3,
-    push_to_hub=True,
-    hub_model_id="username/my-model",
-)
-
-# Train
-trainer = SFTTrainer(
-    model="Qwen/Qwen2.5-0.5B",
-    train_dataset=dataset,
-    args=config,
-    peft_config=LoraConfig(r=16, lora_alpha=32),
-)
-
-trainer.train()
-trainer.push_to_hub()
-```
-
-### Step 3: Run on Jobs
-
-```python
-hf_jobs("uv", {
-    "script": "train.py",  # or URL
-    "flavor": "a10g-large",
-    "timeout": "2h",
-    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
-})
-```
-
-## Running Scripts from URLs
-
-UV scripts can be run directly from URLs:
-
-```python
-hf_jobs("uv", {
-    "script": "https://gist.github.com/username/abc123/raw/train.py",
-    "flavor": "a10g-large",
-    "timeout": "2h",
-    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
-})
-```
-
-**Benefits:**
-- Share scripts via GitHub Gists
-- Version control in Git repos
-- Scripts accessible from anywhere
-
-## Working with Local Scripts
-
-⚠️ **Important:** The `hf_jobs("uv", ...)` command does NOT support local file paths directly. You must make scripts accessible via URL.
-
-### Why Local Paths Don't Work
-
-The Jobs API runs in isolated Docker containers without access to your local filesystem. Scripts must be:
-- Publicly accessible URLs, OR
-- Accessible via authentication (HF_TOKEN for private repos)
-
-**Don't:**
-```python
-# ❌ These will all fail
-hf_jobs("uv", {"script": "train.py"})
-hf_jobs("uv", {"script": "./scripts/train.py"})
-hf_jobs("uv", {"script": "/path/to/train.py"})
-```
-
-**Do:**
-```python
-# ✅ These work
-hf_jobs("uv", {"script": "https://huggingface.co/user/repo/resolve/main/train.py"})
-hf_jobs("uv", {"script": "https://raw.githubusercontent.com/user/repo/main/train.py"})
-hf_jobs("uv", {"script": "https://gist.githubusercontent.com/user/id/raw/train.py"})
-```
-
-### Recommended: Upload to Hugging Face Hub
-
-The easiest way to use local scripts is to upload them to a Hugging Face repository:
-
-```bash
-# Create a dedicated scripts repo
-huggingface-cli repo create my-training-scripts --type model
-
-# Upload your script
-huggingface-cli upload my-training-scripts ./train.py train.py
-
-# If you update the script later
-huggingface-cli upload my-training-scripts ./train.py train.py --commit-message "Updated training params"
-
-# Use in jobs
-script_url = "https://huggingface.co/USERNAME/my-training-scripts/resolve/main/train.py"
-
-hf_jobs("uv", {
-    "script": script_url,
-    "flavor": "a10g-large",
-    "timeout": "2h",
-    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
-})
-```
-
-**Benefits:**
-- ✅ Version control via Git
-- ✅ Private repos supported (with HF_TOKEN)
-- ✅ Easy to share and update
-- ✅ No external dependencies
-- ✅ Integrates with HF ecosystem
-
-**For Private Scripts:**
-```python
-# Your script is in a private repo
-hf_jobs("uv", {
-    "script": "https://huggingface.co/USERNAME/private-scripts/resolve/main/train.py",
-    "flavor": "a10g-large",
-    "secrets": {"HF_TOKEN": "$HF_TOKEN"}  # Allows access to private repo
-})
-```
-
-### Alternative: GitHub Gist
-
-For quick scripts or one-off experiments:
-
-```bash
-# 1. Create a gist at https://gist.github.com
-# 2. Paste your script
-# 3. Click "Create public gist" (or secret gist)
-# 4. Click the "Raw" button to get the raw URL
-
-# Use in jobs
-hf_jobs("uv", {
-    "script": "https://gist.githubusercontent.com/username/gist-id/raw/train.py",
-    "flavor": "a10g-large"
-})
-```
-
-**Benefits:**
-- ✅ Quick and easy
-- ✅ No HF CLI setup needed
-- ✅ Good for sharing examples
-
-**Limitations:**
-- ❌ Less version control than Git repos
-- ❌ Secret gists are still publicly accessible via URL
-
-
-## Using TRL Example Scripts
-
-TRL provides maintained scripts that are UV-compatible:
-
-```python
-hf_jobs("uv", {
-    "script": "https://raw.githubusercontent.com/huggingface/trl/main/examples/scripts/sft.py",
-    "script_args": [
-        "--model_name_or_path", "Qwen/Qwen2.5-0.5B",
-        "--dataset_name", "trl-lib/Capybara",
-        "--output_dir", "my-model",
-        "--push_to_hub",
-        "--hub_model_id", "username/my-model"
-    ],
-    "flavor": "a10g-large",
-    "timeout": "2h",
-    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
-})
-```
-
-**Available TRL scripts:**
-- `sft.py` - Supervised fine-tuning
-- `dpo.py` - Direct Preference Optimization
-- `kto.py` - KTO training
-- `grpo.py` - GRPO training
-- `reward.py` - Reward model training
-- `prm.py` - Process reward model
-
-All at: https://github.com/huggingface/trl/tree/main/examples/scripts
-
-## Best Practices
-
-### 1. Pin Versions
-
-Always pin dependency versions for reproducibility:
-
-```python
-# /// script
-# dependencies = [
-#     "trl==0.12.0",           # Exact version
-#     "transformers>=4.36.0",  # Minimum version
-# ]
-# ///
-```
-
-### 2. Add Logging
-
-Include progress logging for monitoring:
-
-```python
-print("✅ Dataset loaded")
-print("🚀 Starting training...")
-print(f"📊 Training on {len(dataset)} examples")
-```
-
-### 3. Validate Inputs
-
-Check dataset and configuration before training:
-
-```python
-dataset = load_dataset("trl-lib/Capybara", split="train")
-assert len(dataset) > 0, "Dataset is empty!"
-print(f"✅ Dataset loaded: {len(dataset)} examples")
-```
-
-### 4. Add Comments
-
-Document the script for future reference:
-
-```python
-# Train Qwen-0.5B on Capybara dataset using LoRA
-# Expected runtime: ~2 hours on a10g-large
-# Cost estimate: ~$6-8
-```
-
-### 5. Test Locally First
-
-Test scripts locally before running on Jobs:
-
-```bash
-uv run train.py  # Runs locally with uv
-```
-
-## Docker Images
-
-### Default Image
-
-UV scripts run on default Python image with UV installed.
-
-### TRL Image
-
-Use official TRL image for faster startup:
-
-```python
-hf_jobs("uv", {
-    "script": "train.py",
-    "image": "huggingface/trl",  # Pre-installed TRL dependencies
-    "flavor": "a10g-large",
-    "timeout": "2h",
-    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
-})
-```
-
-**Benefits:**
-- Faster job startup (no pip install)
-- All TRL dependencies pre-installed
-- Tested and maintained by HF
-
-## Template Scripts
-
-### Basic SFT Template
-
-```python
-# /// script
-# dependencies = ["trl>=0.12.0"]
-# ///
-
-from datasets import load_dataset
-from trl import SFTTrainer, SFTConfig
-
-dataset = load_dataset("DATASET_NAME", split="train")
-
-trainer = SFTTrainer(
-    model="MODEL_NAME",
-    train_dataset=dataset,
-    args=SFTConfig(
-        output_dir="OUTPUT_DIR",
-        num_train_epochs=3,
-        push_to_hub=True,
-        hub_model_id="USERNAME/MODEL_NAME",
-    )
-)
-
-trainer.train()
-trainer.push_to_hub()
-```
-
-### SFT with LoRA Template
-
-```python
-# /// script
-# dependencies = ["trl>=0.12.0", "peft>=0.7.0"]
-# ///
-
-from datasets import load_dataset
-from peft import LoraConfig
-from trl import SFTTrainer, SFTConfig
-
-dataset = load_dataset("DATASET_NAME", split="train")
-
-trainer = SFTTrainer(
-    model="MODEL_NAME",
-    train_dataset=dataset,
-    peft_config=LoraConfig(r=16, lora_alpha=32),
-    args=SFTConfig(
-        output_dir="OUTPUT_DIR",
-        num_train_epochs=3,
-        push_to_hub=True,
-        hub_model_id="USERNAME/MODEL_NAME",
-    )
-)
-
-trainer.train()
-trainer.push_to_hub()
-```
-
-### DPO Template
-
-```python
-# /// script
-# dependencies = ["trl>=0.12.0"]
-# ///
-
-from datasets import load_dataset
-from transformers import AutoTokenizer
-from trl import DPOTrainer, DPOConfig
-
-model_name = "MODEL_NAME"
-dataset = load_dataset("DATASET_NAME", split="train")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-trainer = DPOTrainer(
-    model=model_name,
-    train_dataset=dataset,
-    tokenizer=tokenizer,
-    args=DPOConfig(
-        output_dir="OUTPUT_DIR",
-        num_train_epochs=3,
-        push_to_hub=True,
-        hub_model_id="USERNAME/MODEL_NAME",
-    )
-)
-
-trainer.train()
-trainer.push_to_hub()
-```
-
-## Troubleshooting
-
-### Issue: Dependencies not installing
-**Check:** Verify dependency names and versions are correct
-
-### Issue: Script not found
-**Check:** Verify URL is accessible and points to raw file
-
-### Issue: Import errors
-**Solution:** Add missing dependencies to `dependencies` list
-
-### Issue: Slow startup
-**Solution:** Use `image="huggingface/trl"` for pre-installed dependencies