Instructions to use chendren/phi2-multi-issue-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chendren/phi2-multi-issue-analysis with Transformers:

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

pipe = pipeline("text-generation", model="chendren/phi2-multi-issue-analysis")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chendren/phi2-multi-issue-analysis")
model = AutoModelForCausalLM.from_pretrained("chendren/phi2-multi-issue-analysis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chendren/phi2-multi-issue-analysis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chendren/phi2-multi-issue-analysis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chendren/phi2-multi-issue-analysis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chendren/phi2-multi-issue-analysis

SGLang

How to use chendren/phi2-multi-issue-analysis 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 "chendren/phi2-multi-issue-analysis" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chendren/phi2-multi-issue-analysis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "chendren/phi2-multi-issue-analysis" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chendren/phi2-multi-issue-analysis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chendren/phi2-multi-issue-analysis with Docker Model Runner:
```
docker model run hf.co/chendren/phi2-multi-issue-analysis
```

Phi-2 Multi-Issue Transcript Analysis (legacy)

Fine-tune of microsoft/phi-2 that emits JSON for multi-issue customer-service transcripts (primary/secondary issues, sentiment, agent rating, resolution, CSAT-style score).

Legacy. No held-out metric was published. Phi-2 is a 2023 2.7B checkpoint. For process-control labels and forbidden-hit scoring use Immortalis/cx-comply. For applied CX decisions with measured accuracy use chendren/cx-decider-applied-v1.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("chendren/phi2-multi-issue-analysis")
tokenizer = AutoTokenizer.from_pretrained("chendren/phi2-multi-issue-analysis")
transcript = "[customer service transcript]"
inputs = tokenizer(transcript, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Intended use

Research on structured transcript tagging. Not a production QA scorer. Agent-performance ratings from this model should not be used for HR decisions.

Limits

No published test accuracy, ECE, or agreement vs human labels
English, clear dialogue format
CSAT values are model guesses, not survey scores

Citation

@misc{phi2-multi-issue-analysis,
  author = {Hendren, Chad},
  title = {Phi-2 Multi-Issue Transcript Analysis Model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/chendren/phi2-multi-issue-analysis}
}

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Safetensors

Model size

3B params

Tensor type

F32

Model tree for chendren/phi2-multi-issue-analysis

Base model

microsoft/phi-2

Finetuned

(413)

this model