What are the hardware requirements for Speculor 2.7B?

Speculor 2.7B requires approximately 5.6 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Speculor 2.7B and how large is it?

Speculor 2.7B is developed by antiven0m, a model with 2.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Speculor 2.7B?

Speculor 2.7B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Speculor 2.7B by antiven0m — VRAM 5.6GB, 2K context

Name: Speculor 2.7B
Author: antiven0m

Speculor 2.7B is an open-source language model by antiven0m. Features: 2.7b LLM, VRAM: 5.6GB, Context: 2K, License: mit, LLM Explorer Score: 0.11.

Conversational Custom code En Endpoints compatible Medical Phi Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/antiven0m/speculor-2.7b

Speculor 2.7B Parameters and Internals

Model Type

text generation

Use Cases

Areas:

mental health support

Primary Use Cases:

Practice Opening Up, Exploratory Tool, Available 24/7

Limitations:

No Medical Expertise, Lack of Human Context, Limited Knowledge, No Crisis Intervention

Additional Notes

The model is available 24/7, easy to access, and an exploratory tool for mental health support but should not replace professional services.

Supported Languages

en (proficient)

Training Details

Data Sources:

counseling Q&A platforms from sources such as WebMD, synthetic mental health conversations, general instructions, personification data

Safety Evaluation

Ethical Considerations:

This LLM is intended as a supportive tool and is not a substitute for professional mental health services or licensed therapists. Users should approach their conversations with appropriate expectations.

Responsible Ai Considerations

Mitigation Strategies:

Flagging System for crisis indicators and Third-Party Moderation by trained human moderators.

Input Output

Accepted Modalities:

text

LLM Name	Speculor 2.7B
Repository 🤗	https://huggingface.co/antiven0m/speculor-2.7b
Model Size	2.7b
Required VRAM	5.6 GB
Updated	2026-08-05
Maintainer	antiven0m
Model Type	phi
Model Files	2.0 GB: 1-of-3 2.0 GB: 2-of-3 1.6 GB: 3-of-3
Supported Languages	en
Model Architecture	PhiForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.38.2
Tokenizer Class	CodeGenTokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	51200
Torch Data Type	float16
Errors	replace

Rank the Speculor 2.7B capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Speculor 2.7B

Best Alternatives	Context / RAM	Downloads	Likes
PhigRange DPO	2K / 5.6 GB	12	1
PhigRange 2.7B Slerp	2K / 5.6 GB	7	0
Damysus 2.7B Chat	2K / 11.1 GB	68	4
JSL MedPhi2 2.7B	2K / 5.6 GB	25	3
...atural Language To Cypher 2.7B	2K / 5.6 GB	7	0
Codephi 2.7B	2K / 5.6 GB	5	0
Phi 2.7B MERGED	0K / 5.6 GB	5	0
Samanthav2 2.7B	0K / 5.6 GB	3	1
Barcenas 2.7B	0K / 5.6 GB	19	1
Damysus 2.7B Chat GGUF	2K / 1.7 GB	160	0

Note: green Score (e.g. "73.2") means that the model is better than antiven0m/speculor-2.7b.