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docs: Unify model examples to use trl-lib namespace #4431

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580c6bb
docs: Unify model examples to use trl-lib namespace
behroozazarkhalili Nov 2, 2025
91e540c
Merge branch 'main' into docs/unify-trl-lib-namespace
behroozazarkhalili Nov 3, 2025
6f906d5
Apply suggestion from @qgallouedec
qgallouedec Nov 4, 2025
800a4d9
Merge branch 'main' into docs/unify-trl-lib-namespace
behroozazarkhalili Nov 4, 2025
c34de94
docs: Use official Qwen model instead of trl-lib namespace
behroozazarkhalili Nov 5, 2025
5dfb2db
docs: Use Qwen3-0.6B model as requested by reviewer
behroozazarkhalili Nov 5, 2025
9bf8db4
Merge branch 'main' into docs/unify-trl-lib-namespace
behroozazarkhalili Nov 5, 2025
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6 changes: 3 additions & 3 deletions docs/source/use_model.md
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ If you have fine-tuned a model fully, meaning without the use of PEFT you can si
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name_or_path = "kashif/stack-llama-2" #path/to/your/model/or/name/on/hub
model_name_or_path = "Qwen/Qwen3-0.6B" #path/to/your/model/or/name/on/hub
device = "cpu" # or "cuda" if you have a GPU

model = AutoModelForCausalLM.from_pretrained(model_name_or_path).to(device)
Expand All @@ -25,7 +25,7 @@ Alternatively you can also use the pipeline:
```python
from transformers import pipeline

model_name_or_path = "kashif/stack-llama-2" #path/to/your/model/or/name/on/hub
model_name_or_path = "Qwen/Qwen3-0.6B" #path/to/your/model/or/name/on/hub
pipe = pipeline("text-generation", model=model_name_or_path)
print(pipe("This movie was really")[0]["generated_text"])
```
Expand All @@ -36,7 +36,7 @@ print(pipe("This movie was really")[0]["generated_text"])
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_name = "kashif/stack-llama-2" #path/to/your/model/or/name/on/hub
base_model_name = "Qwen/Qwen3-0.6B" #path/to/your/model/or/name/on/hub
adapter_model_name = "path/to/my/adapter"

model = AutoModelForCausalLM.from_pretrained(base_model_name)
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