Instructions to use Delta-Vector/Francois-PE-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Francois-PE-V2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Delta-Vector/Francois-PE-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Francois-PE-V2")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Francois-PE-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Delta-Vector/Francois-PE-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delta-Vector/Francois-PE-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Francois-PE-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delta-Vector/Francois-PE-V2

SGLang

How to use Delta-Vector/Francois-PE-V2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Delta-Vector/Francois-PE-V2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Francois-PE-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Delta-Vector/Francois-PE-V2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Francois-PE-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delta-Vector/Francois-PE-V2 with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/Francois-PE-V2
```

Delta-Vector commited on May 4, 2025

Commit

9f67b5b

verified ·

1 Parent(s): 599f080

Update README.md

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README.md +32 -23

README.md CHANGED Viewed

@@ -3,32 +3,41 @@ base_model:
 - PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
 library_name: transformers
 tags:
-- mergekit
-- merge
 ---
 # Francois-PE
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the Passthrough merge method using [PocketDoc/Dans-PersonalityEngine-V1.1.0-12b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.1.0-12b) + /home/mango/Loras/francois as a base.
-### Models Merged
-The following models were included in the merge:
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-base_model: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b+/home/mango/Loras/francois
-dtype: bfloat16
-merge_method: passthrough
-models:
-  - model: PocketDoc/Dans-PersonalityEngine-V1.1.0-12b+/home/mango/Loras/francois
 ```

 - PocketDoc/Dans-PersonalityEngine-V1.1.0-12b
 library_name: transformers
 tags:
+- axolotl
+- finetune
+- roleplaying
+- RP
+- Mistral
 ---
 # Francois-PE
+This is the base model for Francois-PE-Huali, I'd reccomend using that instead of this, The model is extremely underfit and via KTO - was fixed and wayyyyyyyyyyy more coherent
+List of datasets:
 ```
+datasets:
+- PocketDoc/Dans-Personamaxx-VN
+- NewEden/LIMARP-Complexity
+- NewEden/PIPPA-Mega-Filtered
+- NewEden/OpenCAI-ShareGPT
+- NewEden/Creative_Writing-Complexity
+- NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed
+- PocketDoc/Dans-Failuremaxx-Adventure-3
+- NewEden/Books-V2-ShareGPT
+- NewEden/Deepseek-V3-RP-Filtered
+- NewEden/BlueSky-10K-Complexity
+- NewEden/Final-Alpindale-LNs-ShareGPT
+- NewEden/DeepseekRP-Filtered
+- NewEden/RP-logs-V2-Experimental
+- anthracite-org/kalo_opus_misc_240827
+- anthracite-org/kalo_misc_part2
+- NewEden/vanilla-backrooms-claude-sharegpt
+- NewEden/Storium-Prefixed-Clean
+- NewEden/KTO-IF-Dans
+- NewEden/KTO-Instruct-Mix
+- NewEden/Opus-accepted-hermes-rejected-shuffled
+```
+KTO wandb: https://wandb.ai/new-eden/Francois-V2/runs/f2qejmu0?nw=nwuserdeltavector
+SFT wandb: https://wandb.ai/new-eden/Francois/runs/sn3utrs1?nw=nwuserdeltavector