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RichardErkhov/
AliMaatouk_-_Phi-1.5-Tele-gguf

Name: AliMaatouk_-_Phi-1.5-Tele-gguf
Availability: InStock

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Phi-1.5-Tele - GGUF

Model creator: https://huggingface.co/AliMaatouk/
Original model: https://huggingface.co/AliMaatouk/Phi-1.5-Tele/

Name	Quant method	Size
Phi-1.5-Tele.Q2_K.gguf	Q2_K	0.54GB
Phi-1.5-Tele.IQ3_XS.gguf	IQ3_XS	0.59GB
Phi-1.5-Tele.IQ3_S.gguf	IQ3_S	0.61GB
Phi-1.5-Tele.Q3_K_S.gguf	Q3_K_S	0.61GB
Phi-1.5-Tele.IQ3_M.gguf	IQ3_M	0.64GB
Phi-1.5-Tele.Q3_K.gguf	Q3_K	0.69GB
Phi-1.5-Tele.Q3_K_M.gguf	Q3_K_M	0.69GB
Phi-1.5-Tele.Q3_K_L.gguf	Q3_K_L	0.75GB
Phi-1.5-Tele.IQ4_XS.gguf	IQ4_XS	0.74GB
Phi-1.5-Tele.Q4_0.gguf	Q4_0	0.77GB
Phi-1.5-Tele.IQ4_NL.gguf	IQ4_NL	0.78GB
Phi-1.5-Tele.Q4_K_S.gguf	Q4_K_S	0.78GB
Phi-1.5-Tele.Q4_K.gguf	Q4_K	0.83GB
Phi-1.5-Tele.Q4_K_M.gguf	Q4_K_M	0.83GB
Phi-1.5-Tele.Q4_1.gguf	Q4_1	0.85GB
Phi-1.5-Tele.Q5_0.gguf	Q5_0	0.92GB
Phi-1.5-Tele.Q5_K_S.gguf	Q5_K_S	0.92GB
Phi-1.5-Tele.Q5_K.gguf	Q5_K	0.96GB
Phi-1.5-Tele.Q5_K_M.gguf	Q5_K_M	0.96GB
Phi-1.5-Tele.Q5_1.gguf	Q5_1	1.0GB
Phi-1.5-Tele.Q6_K.gguf	Q6_K	1.09GB
Phi-1.5-Tele.Q8_0.gguf	Q8_0	1.41GB

Original model description:

license: mit language:

en pipeline_tag: text-generation tags:
nlp

Phi-1.5-Tele Model Card

Model Summary

The language model Phi-1.5-Tele is a Transformer with 1.3 billion parameters, specialized in telecommunications. It is based on Microsoft phi-1.5 and was continutally pretrained on Tele-Data, a large-scale dataset of approximately 2.5 billion tokens of telecommunications material, including articles, standards, and general web content related to the telecommunications domain.

When assessed against telecommunications benchmarks such as Tele-Eval, Phi-1.5-Tele outperforms phi-1.5 by several percentage points. Additionally, Phi-1.5-Tele matches phi-1.5 across benchmarks related to common sense, language understanding, and logical reasoning. Thus, this adaptation was achieved with minimal compromise in performance on the original version.

Context Length

The model was trained on a context length of 2048 tokens.

Usage

Phi-1.5-Tele is a base model best suited for fine-tuning on applications related to telecommunications. Although it has not been specifically fine-tuned to follow instructions, it can be prompted to answer questions and follow instructions using the following format:

markdown

Write me a poem about telecommunications.

Answer: Our world is a network of digital streams, 
Connecting every voice and thought,
Through the wires and fibers that transmit,
Bringing us closer to the end of the road.

where the model generates the text after "Answer:".

Sample Code

Below we share some code snippets on how to get quickly started with running the model. First, make sure to pip install transformers, then copy the snippet corresponding to your hardware and adapt it to your usecase.

Running the model on a CPU

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("AliMaatouk/Phi-1.5-Tele", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Phi-1.5-Tele")

prompt = "Write me a poem about telecommunications.\nAnswer:"
input_ids = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**input_ids, max_new_tokens=100)

generated_tokens = outputs[0, len(input_ids['input_ids'][0]):]
response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(response)

Running the model on a single / multi GPU