Text Generation
Transformers
Safetensors
English
talkie
causal-lm
custom-code
Merge
conversational
custom_code
Instructions to use Abstract4700/frankentalkie-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abstract4700/frankentalkie-20b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Abstract4700/frankentalkie-20b", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Abstract4700/frankentalkie-20b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Abstract4700/frankentalkie-20b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Abstract4700/frankentalkie-20b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Abstract4700/frankentalkie-20b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Abstract4700/frankentalkie-20b
- SGLang
How to use Abstract4700/frankentalkie-20b 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 "Abstract4700/frankentalkie-20b" \ --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": "Abstract4700/frankentalkie-20b", "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 "Abstract4700/frankentalkie-20b" \ --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": "Abstract4700/frankentalkie-20b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Abstract4700/frankentalkie-20b with Docker Model Runner:
docker model run hf.co/Abstract4700/frankentalkie-20b
frankentalkie
A merge derived from
talkie-lm/talkie-1930-13b-it.
using MergeMonster (https://github.com/Gryphe/MergeMonster)
The checkpoint keeps Talkie's tokenizer, embeddings, LM head, and custom Transformers
runtime, then relayers the decoder overlapping mid-stack
recipe. The retained model expands Talkie from 40 to 60 blocks using:
[0-11] + [12-23] + [14-25] + [16-27] + [28-39]
Repeated blocks use damped residual gains.
Usage
Install the runtime dependencies first:
pip install "transformers>=5.6.0" "accelerate>=1.0.0" "safetensors>=0.5.0" "tiktoken>=0.6.0"
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "Abstract4700/frankentalkie"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo,
trust_remote_code=True,
dtype="bfloat16",
device_map="auto",
)
messages = [
{"role": "user", "content": "Write an essay predicting what life will be like in the year 1960."}
]
inputs = tok.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
out = model.generate(inputs, max_new_tokens=300, do_sample=True, temperature=0.7)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
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Model tree for Abstract4700/frankentalkie-20b
Base model
talkie-lm/talkie-1930-13b-base Finetuned
talkie-lm/talkie-1930-13b-it